{ "count" : 15856, "data" : [ { "id" : "6a2176d974d8ee00a0c1b207", "title" : "FR Annotations Auditor, Community Feedback Annotation Team", "company" : "Amazon Development Centre (South Africa) (Proprietary) Limited", "industry" : "Customer Service, Fulfillment Center, customer service", "salary" : { "min" : 215000.0, "max" : 305000.0, "currency" : "ZAR", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for an entry-level FR Annotations Auditor in Cape Town, South Africa, adjusted for the technology industry." }, "location" : "Cape Town, Western Cape, ZAF", "locations" : [ "Cape Town, Western Cape, ZAF" ], "timestamp" : "2026-06-14T23:44:09.190", "fingerprint" : "23b61d8fe82765bd396dca5d136e8b4124d462cba3d258301457870d6fe4bbfd", "source" : "amazon", "language" : "en", "applyUrl" : "https://account.amazon.jobs/jobs/10437932/apply", "externalId" : "5e2b007e-706f-45ee-933e-9d984b812554", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Cape Town", "state" : "Western Cape", "country" : "South Africa", "continent" : "af", "source" : "llm", "workMode" : "onsite", "geo" : "-33.9288301,18.4172197" } ], "locationText" : "Cape Town, Western Cape, South Africa", "employmentType" : "FULL_TIME", "experienceMinYears" : 1, "technicalSkills" : "MS Office, Natural Language Processing", "professionalSkills" : [ "Quality Assurance", "Data Integrity", "Linguistic expertise" ], "hardSkillsList" : [ "Annotation review", "Quality checks" ], "hardSkills" : [ { "name" : "Annotation review", "level" : "intermediate" }, { "name" : "Quality checks", "level" : "intermediate" } ], "majorSkills" : "Natural Language Processing, Quality Assurance", "majorSkillsList" : [ "Natural Language Processing", "Quality Assurance" ], "coreResponsibilities" : "Review annotations for consistency, Review annotations for accuracy, Review annotations for adherence to guidelines, Provide constructive feedback, Understand and annotate texts, Ensure accuracy of annotated data, Ensure integrity of annotated data, Perform regular quality checks, Identify inconsistencies, Address inconsistencies, Maintain data cleanliness, Evaluate labels, Evaluate annotated data", "domainKnowledge" : "Natural language processing; Generative AI models", "domainList" : [ "natural-language-processing", "generative-ai-models" ], "domainSpecificKnowledge" : [ "Natural language processing", "Generative AI models" ], "toolProficiency" : [ "MS Office" ], "problemSolvingSkills" : "Identify inconsistencies; Address inconsistencies", "specialRequirements" : "Verify ability to work lawfully in South Africa", "qualificationsBasic" : "Experience as Quality Specialist, Experience as Quality Analyst, Proficiency in MS Office, 6 to 9 months in Annotations role", "qualificationsPreferred" : "Fluent French and English, Experience in machine learning, Experience in data mining, Experience in information retrieval, Experience in statistics, Experience in natural language processing, Prior experience in linguistic annotation, Prior experience in data labeling", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "Our overall mission is simple: to be Earth’s most customer-centric company. We want Amazon to be the place where customers can find, discover, and buy anything online. Whatever our customers want, we will find the means to deliver it. With your help, Amazon will deliver world-class AI-generated experiences to our customers. We are seeking a dedicated, fluent French-speaking Auditor to join our Community Feedback Annotations team and contribute to the development and enhancement of advanced natural language processing models. As an Auditor, you will evaluate annotated datasets used to train and improve generative AI models. Your meticulous attention to detail and linguistic expertise will play a crucial role in ensuring the accuracy and effectiveness of our models. Key job responsibilities Quality Assurance Review and verify annotations made by fellow annotators for consistency, accuracy, and adherence to guidelines. Provide constructive feedback to maintain annotation quality. Domain Expertise Gain proficiency in understanding and annotating texts across various experiences and organizations, supporting the creation of specialized models. Data Integrity Ensure accuracy and integrity of annotated data through regular quality checks. Identify and address inconsistencies, maintaining a high standard of data cleanliness. A day in the life You will evaluate labels and annotated data for various projects across our organization. Some projects will run longer than others, but expect a variety of evaluation requests month to month. About the team Our team is passionate about human motivation and behavior and uses machine learning, classification algorithms, and scalable platform services to have a cross-org impact every day. We evaluate user research, customer experience knowledge, data, and cross-Amazon moderation expertise to ensure other teams think about customer trust. Basic Qualifications: - Experience as a Quality Specialist and/or Quality Analyst - Experience with computer skills, including proficiency in MS Office (Word, Excel, PowerPoint) - 6 to 9 months in an Annotations role or similar - Not currently on attendance probation or any conduct warnings in the last 3 months Preferred Qualifications: - Speak, write, and read fluently in French and English with respect to complex technical instructions and concepts - Experience in machine learning, data mining, information retrieval, statistics or natural language processing - Prior experience in linguistic annotation, data labeling, or similar roles is a plus. Amazon is an equal opportunities employer, and we value your passion to discover, invent, simplify and build. We welcome applications from all members of society irrespective of age, sex, disability, sexual orientation, race, religion or belief. Amazon is strongly committed to diversity within its community and especially welcomes applications from South African citizens who are members of designated groups who may contribute to Employment Equity within the workplace and the further diversification of ideas. In this regard, the relevant laws and principles associated with Employment Equity will be considered when appointing potential candidates. We are required by law to verify your ability to work lawfully in South Africa. Amazon requires that you submit a copy of either your identity document or your passport and any applicable work permit if you are a foreign national, along with an updated curriculum vitae. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.", "summaryDescription" : "FR Annotations Auditor, Community Feedback Annotation Team at Amazon Development Centre (South Africa) (Proprietary) Limited. Skills: Natural Language Processing, Quality Assurance. Review annotations for consistency. Review annotations for accuracy", "postedDate" : "2026-06-13T06:14:47.562", "createdAt" : "2026-06-13T06:14:47.562", "lastUpdated" : "2026-06-14T23:44:09.190", "ingestTimestamp" : "2026-06-13T06:14:47.562", "mongoId" : "6a2176d974d8ee00a0c1b207", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "sourceUrl" : "https://account.amazon.jobs/jobs/10437932/apply", "salaryBand" : "200k+", "hasSalary" : false, "allSkills" : [ "Annotation review", "Quality checks", "Natural Language Processing", "Quality Assurance", "Data Integrity", "Linguistic expertise", "Natural language processing", "Generative AI models", "MS Office" ], "skillCount" : 9, "jobCategory" : "customer-service", "dataQualityScore" : 0.8888889, "_score" : 2726.6938, "daysOld" : 1, "hasLocation" : true, "hasGeo" : true }, { "id" : "6a2c6ace74d8ee00a090e155", "title" : "Data Annotator", "company" : "Innodata Inc.", "industry" : "Technology", "salary" : { "min" : 15000.0, "max" : 25000.0, "currency" : "BGN", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Entry-level Data Annotator compensation in Bulgaria, adjusted for a part-time remote role in the technology industry." }, "location" : "ISL - Remote - US", "locations" : [ "ISL - Remote - US" ], "timestamp" : "2026-06-16T05:15:03.250", "fingerprint" : "7ec94f33947c2d9ecc0224266f4047f6c4d3d59572c1bba505186556883a4928", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/innodatainc/jobs/4235828009", "externalId" : "4235828009", "industryFunction" : { }, "structuredLocations" : [ { "country" : "Bulgaria", "continent" : "eu", "workMode" : "remote" } ], "locationText" : "Bulgaria", "employmentType" : "PART_TIME", "minimumEducation" : "High School", "coreResponsibilities" : "Rate/assess AI model performance, Label content elements, Assign predefined categories or labels, Evaluate content quality/appropriateness, Generate labels for concept understanding, Create training data by transforming data, Review data and identify product feature function, Label model outputs, Order or rank items, Create prompts for AI systems, Evaluate content relevance, Generate responses to prompts, Rewrite existing text, Produce concise summaries, Convert spoken language to text, Convert content between languages, Gather and compile data for AI models", "domainKnowledge" : "Generative AI; Large language models", "domainList" : [ "generative-ai", "large-language-models" ], "domainSpecificKnowledge" : [ "Generative AI", "Large language models" ], "qualificationsBasic" : "High School Diploma or higher, Professional or Expert level proficiency (C1/C2) in English", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Data Annotators to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program. This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. What You’ll Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. You’ll Thrive in This Role If You Have: A High School Diploma or higher is required. Professional or Expert level proficiency (C1/C2) in English The expected hourly salary range for this position is $13 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.", "summaryDescription" : "Data Annotator at Innodata Inc.. Rate/assess AI model performance. Label content elements", "postedDate" : "2026-06-14T20:03:44.730", "createdAt" : "2026-06-14T20:03:44.730", "lastUpdated" : "2026-06-16T05:15:03.250", "ingestTimestamp" : "2026-06-14T20:03:44.730", "mongoId" : "6a2c6ace74d8ee00a090e155", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryBand" : "<30k", "hasSalary" : false, "allSkills" : [ "Generative AI", "Large language models" ], "skillCount" : 2, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 1174.3582, "daysOld" : 1, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2c6ace74d8ee00a090e15f", "title" : "Data Annotator", "company" : "Innodata Inc.", "industry" : "Technology", "salary" : { "min" : 31000.0, "max" : 45000.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for Entry-level Data Annotator in the US, adjusted for part-time employment." }, "location" : "ISL - Remote - US", "locations" : [ "ISL - Remote - US" ], "timestamp" : "2026-06-16T00:35:22.109", "fingerprint" : "13d41f4e12f1d70197e75a5fc7205669e36c7b2ea9413cee46ee42c1e0c3e49a", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/innodatainc/jobs/4232469009", "externalId" : "4232469009", "industryFunction" : { }, "structuredLocations" : [ { "country" : "United States", "continent" : "na", "workMode" : "remote" } ], "locationText" : "United States", "employmentType" : "PART_TIME", "minimumEducation" : "High School", "coreResponsibilities" : "Rate/assess AI model performance, Label content elements, Assign predefined categories or labels, Evaluate perceived quality/appropriateness of content, Generate labels to advance concept understanding, Create training data by applying transformations, Modify images (rotation, flipping, cropping), Generate new text (paraphrasing, summarization), Alter audio/video signals (speed modification, pitch shifting), Review data and identify product feature function, Label model outputs, Identify gaming, Identify branded content, Order or rank items based on criteria, Create prompts or questions for AI systems, Evaluate content relevance, Generate responses to prompts or questions, Rewrite existing text preserving original meaning, Produce concise summaries of text or data, Convert spoken language to written text, Convert text or spoken language from one language, Gather and compile data for AI models, Compile text, images, videos, audio files", "domainKnowledge" : "Generative AI; Large language models", "domainList" : [ "generative-ai", "large-language-models" ], "domainSpecificKnowledge" : [ "Generative AI", "Large language models" ], "qualificationsBasic" : "High School Diploma or higher, Professional or Expert level proficiency (C1/C2) in English", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Data Annotators to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program. This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. What You’ll Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. You’ll Thrive in This Role If You Have: A High School Diploma or higher is required. Professional or Expert level proficiency (C1/C2) in English The expected hourly salary range for this position is $9 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.", "summaryDescription" : "Data Annotator at Innodata Inc.. Rate/assess AI model performance. Label content elements", "postedDate" : "2026-06-14T20:02:39.234", "createdAt" : "2026-06-14T20:02:39.234", "lastUpdated" : "2026-06-16T00:35:22.109", "ingestTimestamp" : "2026-06-14T20:02:39.234", "mongoId" : "6a2c6ace74d8ee00a090e15f", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 31000, "salaryAnnualMax" : 45000, "salaryBand" : "30k-50k", "hasSalary" : true, "allSkills" : [ "Generative AI", "Large language models" ], "skillCount" : 2, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 1174.2518, "daysOld" : 1, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2c6ace74d8ee00a090e161", "title" : "Data Annotator", "company" : "Innodata Inc.", "industry" : "Technology", "salary" : { "min" : 31000.0, "max" : 45000.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for Entry level Data Annotator in the US, adjusted for part-time employment." }, "location" : "ISL - Remote - US", "locations" : [ "ISL - Remote - US" ], "timestamp" : "2026-06-16T02:40:34.194", "fingerprint" : "05fbbbb7ef751e564376b00b473c03750f2b12f76b320024dccb9dd447601dd0", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/innodatainc/jobs/4234822009", "externalId" : "4234822009", "industryFunction" : { }, "structuredLocations" : [ { "country" : "United States", "continent" : "na", "workMode" : "remote" } ], "locationText" : "United States", "employmentType" : "PART_TIME", "minimumEducation" : "High School", "coreResponsibilities" : "Rate/assess AI model performance, Label content elements, Assign predefined categories or labels, Evaluate perceived quality/appropriateness of content, Generate labels to advance concept understanding, Create training data by applying transformations, Modify images (rotation, flipping, cropping), Generate new text (paraphrasing, summarization), Alter audio/video signals (speed modification, pitch shifting), Review data and identify product feature function, Label model outputs, Identify gaming, Identify branded content, Order or rank items based on criteria, Create prompts for language models, Evaluate content relevance, Generate responses to prompts, Rewrite existing text, Produce concise summaries, Convert spoken language to text, Convert text from one language to another, Gather and compile data for AI models", "domainKnowledge" : "Generative AI; Large language models", "domainList" : [ "generative-ai", "large-language-models" ], "domainSpecificKnowledge" : [ "Generative AI", "Large language models" ], "qualificationsBasic" : "High School Diploma or higher, Professional or Expert level proficiency (C1/C2) in English", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Data Annotators to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program. This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. What You’ll Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. You’ll Thrive in This Role If You Have: A High School Diploma or higher is required. Professional or Expert level proficiency (C1/C2) in English The expected hourly salary range for this position is $9 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.", "summaryDescription" : "Data Annotator at Innodata Inc.. Rate/assess AI model performance. Label content elements", "postedDate" : "2026-06-14T19:57:50.206", "createdAt" : "2026-06-14T19:57:50.206", "lastUpdated" : "2026-06-16T02:40:34.194", "ingestTimestamp" : "2026-06-14T19:57:50.206", "mongoId" : "6a2c6ace74d8ee00a090e161", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 31000, "salaryAnnualMax" : 45000, "salaryBand" : "30k-50k", "hasSalary" : true, "allSkills" : [ "Generative AI", "Large language models" ], "skillCount" : 2, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 1173.7825, "daysOld" : 1, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2c6ace74d8ee00a090e160", "title" : "Data Annotator", "company" : "Innodata Inc.", "industry" : "Technology", "salary" : { "min" : 31000.0, "max" : 45000.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for Entry-level Data Annotator in the US, adjusted for part-time employment." }, "location" : "ISL - Remote - US", "locations" : [ "ISL - Remote - US" ], "timestamp" : "2026-06-16T04:34:14.360", "fingerprint" : "56ba0b2819dcc4bd5fa25a55bdc7318fe938c274db725c57cbc0f5012889d1d6", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/innodatainc/jobs/4232330009", "externalId" : "4232330009", "industryFunction" : { }, "structuredLocations" : [ { "country" : "United States", "continent" : "na", "workMode" : "remote" } ], "locationText" : "United States", "employmentType" : "PART_TIME", "minimumEducation" : "High School", "coreResponsibilities" : "Rate/assess AI model performance, Label content elements, Assign predefined categories or labels, Evaluate content quality/appropriateness, Generate labels for concept understanding, Create training data by transforming data, Modify images (rotation, flipping, cropping), Generate new text (paraphrasing, summarization), Alter audio/video signals (speed, pitch), Review data and identify product feature function, Label model outputs, Identify gaming, Identify branded content, Order or rank items, Create prompts for AI systems, Evaluate content relevance, Generate responses to prompts, Rewrite text preserving original meaning, Produce concise summaries, Convert spoken language to text, Convert text/spoken language between languages, Gather and compile data for AI models", "domainKnowledge" : "Generative AI; Large language models", "domainList" : [ "generative-ai", "large-language-models" ], "domainSpecificKnowledge" : [ "Generative AI", "Large language models" ], "qualificationsBasic" : "High School Diploma or higher, Professional or Expert level proficiency (C1/C2) in English", "remoteFriendly" : true, "workSiteFlexibility" : "Flexible", "rawDescription" : "Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Data Annotators to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program. This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. What You’ll Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. You’ll Thrive in This Role If You Have: A High School Diploma or higher is required. Professional or Expert level proficiency (C1/C2) in English The expected hourly salary range for this position is $9 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.", "summaryDescription" : "Data Annotator at Innodata Inc.. Rate/assess AI model performance. Label content elements", "postedDate" : "2026-06-14T18:33:03.429", "createdAt" : "2026-06-14T18:33:03.429", "lastUpdated" : "2026-06-16T04:34:14.360", "ingestTimestamp" : "2026-06-14T18:33:03.429", "mongoId" : "6a2c6ace74d8ee00a090e160", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 31000, "salaryAnnualMax" : 45000, "salaryBand" : "30k-50k", "hasSalary" : true, "allSkills" : [ "Generative AI", "Large language models" ], "skillCount" : 2, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 1165.5148, "daysOld" : 1, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2c6ace74d8ee00a090e158", "title" : "Data Annotator", "company" : "Innodata Inc.", "industry" : "Technology", "salary" : { "min" : 200000.0, "max" : 400000.0, "currency" : "INR", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for an entry-level Data Annotator in India, adjusted for part-time employment." }, "location" : "ISL - Remote - US", "locations" : [ "ISL - Remote - US" ], "certifications" : [ "GCP Professional Data Engineer", "AWS Data Analytics", "Databricks Certified", "Dbt Certified" ], "timestamp" : "2026-06-15T09:50:36.888", "fingerprint" : "d478c716c3e8bf3d620c5c8b4707d98b57818f678461ca9d02823d48f0d530a3", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/innodatainc/jobs/4235832009", "externalId" : "4235832009", "industryFunction" : { }, "structuredLocations" : [ { "workMode" : "remote" } ], "locationText" : "", "employmentType" : "PART_TIME", "minimumEducation" : "High School", "technicalSkills" : "Python, SQL, R, Scala, Spark, Dbt, Snowflake, BigQuery, Redshift, Databricks, Fivetran, Airflow, Dbt Cloud, Delta Lake, Scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM, Hugging Face, MLflow, AWS, GCP, Azure", "professionalSkills" : [ "Data annotation", "Data labeling", "Content evaluation", "Data transformation", "Data review", "Data gathering", "Data compilation" ], "hardSkillsList" : [ "Statistical analysis", "Machine learning", "Natural language processing", "Data modeling", "Data engineering", "AI development" ], "hardSkills" : [ { "name" : "Statistical analysis", "level" : "intermediate" }, { "name" : "Machine learning", "level" : "intermediate" }, { "name" : "Natural language processing", "level" : "intermediate" }, { "name" : "Data modeling", "level" : "intermediate" }, { "name" : "Data engineering", "level" : "intermediate" }, { "name" : "AI development", "level" : "intermediate" } ], "majorSkills" : "Data Annotation, AI Development, LLM Training", "majorSkillsList" : [ "Data Annotation", "AI Development", "LLM Training" ], "coreResponsibilities" : "Rate/assess AI model performance, Label content elements, Assign predefined categories/labels, Evaluate content quality/appropriateness, Generate labels for concepts/trends, Create training data, Apply data transformations, Review data for product feature function, Label model outputs, Order or rank items, Create prompts for AI systems, Evaluate content relevance, Generate responses from AI systems, Rewrite existing text, Produce text summaries, Convert audio to text, Translate languages, Gather and compile data", "domainKnowledge" : "Generative AI; Large Language Models; AI systems; Data engineering; Natural language; Reasoning", "domainList" : [ "generative-ai", "large-language-models", "ai-systems", "data-engineering", "natural-language", "reasoning" ], "domainSpecificKnowledge" : [ "Generative AI", "Large Language Models", "AI systems", "Data engineering", "Natural language", "Reasoning" ], "toolProficiency" : [ "Generative AI", "Large Language Models" ], "qualificationsBasic" : "High School Diploma or higher, Professional or Expert level proficiency in English", "qualificationsPreferred" : "PhD preferred, Specific ML framework experience, Cloud platform certs", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Data Annotator to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program. This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. What You’ll Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. You’ll Thrive in This Role If You Have: A High School Diploma or higher is required. Professional or Expert level proficiency (C1/C2) in English The expected hourly salary range for this position is $13 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.", "summaryDescription" : "Data Annotator at Innodata Inc.. Skills: Data Annotation, AI Development, LLM Training. Rate/assess AI model performance. Label content elements", "postedDate" : "2026-06-14T07:57:51.692", "createdAt" : "2026-06-14T07:57:51.692", "lastUpdated" : "2026-06-15T09:50:36.888", "ingestTimestamp" : "2026-06-14T07:57:51.692", "mongoId" : "6a2c6ace74d8ee00a090e158", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryBand" : "200k+", "hasSalary" : false, "allSkills" : [ "Statistical analysis", "Machine learning", "Natural language processing", "Data modeling", "Data engineering", "AI development", "Data Annotation", "AI Development", "LLM Training", "Data annotation", "Data labeling", "Content evaluation", "Data transformation", "Data review", "Data gathering", "Data compilation", "Generative AI", "Large Language Models", "AI systems", "Natural language", "Reasoning", "GCP Professional Data Engineer", "AWS Data Analytics", "Databricks Certified", "Dbt Certified" ], "skillCount" : 25, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 1113.4016, "daysOld" : 1, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2c6ace74d8ee00a090e15b", "title" : "Data Annotator", "company" : "Innodata Inc.", "industry" : "Technology", "salary" : { "min" : 240000.0, "max" : 480000.0, "currency" : "INR", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for an entry-level Data Annotator in India, adjusted for part-time employment." }, "location" : "ISL - Remote - US", "locations" : [ "ISL - Remote - US" ], "timestamp" : "2026-06-15T09:17:30.177", "fingerprint" : "933264fcf3b9b0510c8d9fffde21679e6dd178b2fa3065b3960ac09255e5a639", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/innodatainc/jobs/4232190009", "externalId" : "4232190009", "industryFunction" : { }, "structuredLocations" : [ { "workMode" : "remote" } ], "locationText" : "", "employmentType" : "PART_TIME", "minimumEducation" : "High School", "technicalSkills" : "AI, Generative AI, Large Language Models, Machine Learning", "hardSkillsList" : [ "Data annotation", "Content labeling", "Data transformation", "Content evaluation", "Prompt creation", "Text summarization", "Transcription", "Translation", "Data gathering" ], "hardSkills" : [ { "name" : "Data annotation", "level" : "intermediate" }, { "name" : "Content labeling", "level" : "intermediate" }, { "name" : "Data transformation", "level" : "intermediate" }, { "name" : "Content evaluation", "level" : "intermediate" }, { "name" : "Prompt creation", "level" : "intermediate" }, { "name" : "Text summarization", "level" : "intermediate" }, { "name" : "Transcription", "level" : "intermediate" }, { "name" : "Translation", "level" : "intermediate" }, { "name" : "Data gathering", "level" : "intermediate" } ], "majorSkills" : "Data annotation, AI development, LLM training", "majorSkillsList" : [ "Data annotation", "AI development", "LLM training" ], "coreResponsibilities" : "Rate AI model performance, Label content elements, Assign predefined categories, Evaluate content quality, Generate labels for concepts, Create training data, Review data for product features, Label model outputs, Order or rank items, Create prompts for AI systems, Evaluate content relevance, Generate responses from AI, Rewrite existing text, Produce text summaries, Convert audio to text, Translate languages, Gather and compile data", "domainKnowledge" : "Generative AI; Large Language Models", "domainList" : [ "generative-ai", "large-language-models" ], "domainSpecificKnowledge" : [ "Generative AI", "Large Language Models" ], "qualificationsBasic" : "High School Diploma or higher, Professional or Expert level proficiency in English", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Data Annotators to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program. This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. What You’ll Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. You’ll Thrive in This Role If You Have: A High School Diploma or higher is required. Professional or Expert level proficiency (C1/C2) in English The expected hourly salary range for this position is $9 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.", "summaryDescription" : "Data Annotator at Innodata Inc.. Skills: Data annotation, AI development, LLM training. Rate AI model performance. Label content elements", "postedDate" : "2026-06-14T02:47:49.071", "createdAt" : "2026-06-14T02:47:49.071", "lastUpdated" : "2026-06-15T09:17:30.177", "ingestTimestamp" : "2026-06-14T02:47:49.071", "mongoId" : "6a2c6ace74d8ee00a090e15b", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryBand" : "200k+", "hasSalary" : false, "allSkills" : [ "Data annotation", "Content labeling", "Data transformation", "Content evaluation", "Prompt creation", "Text summarization", "Transcription", "Translation", "Data gathering", "AI development", "LLM training", "Generative AI", "Large Language Models" ], "skillCount" : 13, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 1083.5411, "daysOld" : 1, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2c6ace74d8ee00a090e157", "title" : "Data Annotator", "company" : "Innodata Inc.", "industry" : "Technology", "salary" : { "min" : 200000.0, "max" : 400000.0, "currency" : "INR", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for an entry-level Data Annotator in India, adjusted for part-time employment." }, "location" : "ISL - Remote - US", "locations" : [ "ISL - Remote - US" ], "timestamp" : "2026-06-15T09:32:57.004", "fingerprint" : "59a0e1b7fb106598bc6b2798b593af4e8dac15b77a499ece4ca1f8921f8723c9", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/innodatainc/jobs/4231736009", "externalId" : "4231736009", "industryFunction" : { }, "structuredLocations" : [ { "workMode" : "remote" } ], "locationText" : "", "employmentType" : "PART_TIME", "minimumEducation" : "High School", "technicalSkills" : "AI, Generative AI, Large Language Models, Machine Learning", "hardSkillsList" : [ "Data annotation", "Content labeling", "Data transformation", "Data gathering", "Data compilation", "Text summarization", "Speech to text", "Translation" ], "hardSkills" : [ { "name" : "Data annotation", "level" : "intermediate" }, { "name" : "Content labeling", "level" : "intermediate" }, { "name" : "Data transformation", "level" : "intermediate" }, { "name" : "Data gathering", "level" : "intermediate" }, { "name" : "Data compilation", "level" : "intermediate" }, { "name" : "Text summarization", "level" : "intermediate" }, { "name" : "Speech to text", "level" : "intermediate" }, { "name" : "Translation", "level" : "intermediate" } ], "majorSkills" : "Data Annotation, AI Development, LLM Training", "majorSkillsList" : [ "Data Annotation", "AI Development", "LLM Training" ], "coreResponsibilities" : "Rate AI model performance, Assess AI model behavior, Label content elements, Assign predefined categories, Evaluate content quality, Evaluate content appropriateness, Generate labels for concepts, Generate labels for trends, Create training data, Apply data transformations, Modify images, Generate new text, Alter audio/video signals, Review data, Identify product feature function, Label model outputs, Identify content type, Order items, Rank items, Create prompts for AI, Generate responses from AI, Rewrite existing text, Preserve original meaning, Produce concise summaries, Convert audio to text, Convert language to text, Gather data, Compile data", "domainKnowledge" : "Generative AI; Large Language Models", "domainList" : [ "generative-ai", "large-language-models" ], "domainSpecificKnowledge" : [ "Generative AI", "Large Language Models" ], "qualificationsBasic" : "High School Diploma or higher, Professional or Expert level proficiency in English", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Data Annotators to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program. This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. What You’ll Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. You’ll Thrive in This Role If You Have: A High School Diploma or higher is required. Professional or Expert level proficiency (C1/C2) in English The expected hourly salary range for this position is $9 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.", "summaryDescription" : "Data Annotator at Innodata Inc.. Skills: Data Annotation, AI Development, LLM Training. Rate AI model performance. Assess AI model behavior", "postedDate" : "2026-06-14T01:44:26.636", "createdAt" : "2026-06-14T01:44:26.636", "lastUpdated" : "2026-06-15T09:32:57.004", "ingestTimestamp" : "2026-06-14T01:44:26.636", "mongoId" : "6a2c6ace74d8ee00a090e157", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryBand" : "200k+", "hasSalary" : false, "allSkills" : [ "Data annotation", "Content labeling", "Data transformation", "Data gathering", "Data compilation", "Text summarization", "Speech to text", "Translation", "Data Annotation", "AI Development", "LLM Training", "Generative AI", "Large Language Models" ], "skillCount" : 13, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 1076.3756, "daysOld" : 1, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2c6ace74d8ee00a090e138", "title" : "AI Annotator", "company" : "Innodata Inc.", "industry" : "Technology", "salary" : { "min" : 9.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "ISL - Remote - US", "locations" : [ "ISL - Remote - US" ], "timestamp" : "2026-06-15T13:48:42.162", "fingerprint" : "44ced7e4f0098aedd1f90bc140a3fd8a5e24d57d48d24993286af11fade426c4", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/innodatainc/jobs/4232489009", "externalId" : "4232489009", "industryFunction" : { }, "structuredLocations" : [ { "country" : "United States", "continent" : "na", "workMode" : "remote" } ], "locationText" : "United States", "employmentType" : "PART_TIME", "minimumEducation" : "High School", "technicalSkills" : "AI, LLMs, Machine learning", "professionalSkills" : [ "Data annotation", "Content evaluation", "Data labeling", "Quality assessment", "Data generation", "Data transformation", "Data review", "Data ranking", "Prompt creation", "Content relevance", "Response generation", "Text rewriting", "Summarization", "Transcription", "Translation", "Data gathering", "Data compilation" ], "hardSkillsList" : [ "Language intricacies", "Reasoning skills" ], "hardSkills" : [ { "name" : "Language intricacies", "level" : "intermediate" }, { "name" : "Reasoning skills", "level" : "intermediate" } ], "majorSkills" : "Generative AI, Large language models", "majorSkillsList" : [ "Generative AI", "Large language models" ], "coreResponsibilities" : "Rate AI model performance, Assess AI model behavior, Label content elements, Assign predefined categories, Evaluate content quality, Evaluate content appropriateness, Generate labels for concepts, Create training data, Apply data transformations, Modify images, Generate new text, Alter audio/video signals, Review data, Identify product feature function, Label model outputs, Identify content type, Order items by criteria, Rank items by criteria, Create prompts for AI, Generate responses from AI, Rewrite existing text, Preserve original meaning, Produce concise summaries, Convert audio to text, Convert language to text, Gather data, Compile data", "domainKnowledge" : "Generative AI; Large language models; AI systems", "domainList" : [ "generative-ai", "large-language-models", "ai-systems" ], "domainSpecificKnowledge" : [ "Generative AI", "Large language models", "AI systems" ], "qualificationsBasic" : "High School Diploma or higher, Professional or Expert level proficiency in English", "qualificationsPreferred" : "PhD preferred, Specific ML framework experience, Cloud platform certs", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Data Annotators to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program. This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. What You’ll Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. You’ll Thrive in This Role If You Have: A High School Diploma or higher is required. Professional or Expert level proficiency (C1/C2) in English The expected hourly salary range for this position is $9 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.", "summaryDescription" : "AI Annotator at Innodata Inc.. Skills: Generative AI, Large language models. Rate AI model performance. Assess AI model behavior", "postedDate" : "2026-06-14T04:11:29.134", "createdAt" : "2026-06-14T04:11:29.134", "lastUpdated" : "2026-06-15T13:48:42.162", "ingestTimestamp" : "2026-06-14T04:11:29.134", "mongoId" : "6a2c6ace74d8ee00a090e138", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 18720, "salaryBand" : "<30k", "hasSalary" : true, "allSkills" : [ "Language intricacies", "Reasoning skills", "Generative AI", "Large language models", "Data annotation", "Content evaluation", "Data labeling", "Quality assessment", "Data generation", "Data transformation", "Data review", "Data ranking", "Prompt creation", "Content relevance", "Response generation", "Text rewriting", "Summarization", "Transcription", "Translation", "Data gathering", "Data compilation", "AI systems" ], "skillCount" : 22, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 1074.0383, "daysOld" : 1, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2c6ace74d8ee00a090e135", "title" : "AI Annotation Specialist", "company" : "Innodata Inc.", "industry" : "Technology", "salary" : { "min" : 35000.0, "max" : 50000.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for Entry-level AI Annotation Specialists in the US, adjusted for part-time employment." }, "location" : "ISL - Remote - US", "locations" : [ "ISL - Remote - US" ], "timestamp" : "2026-06-15T21:25:40.257", "fingerprint" : "11337ac1370825ef83977315c3b15a22a59adfd7c158f493a190fdf7478820b6", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/innodatainc/jobs/4232730009", "externalId" : "4232730009", "industryFunction" : { }, "structuredLocations" : [ { "country" : "United States", "continent" : "na", "workMode" : "remote" } ], "locationText" : "United States", "employmentType" : "PART_TIME", "minimumEducation" : "High School", "majorSkills" : "Data Annotation, AI Development, LLM", "majorSkillsList" : [ "Data Annotation", "AI Development", "LLM" ], "coreResponsibilities" : "Rate/assess AI model performance, Label content elements, Assign predefined categories or labels, Evaluate content quality/appropriateness, Generate labels for concept understanding, Create training data by applying transformations, Review data to identify feature functionality, Label model outputs, Order or rank items based on criteria, Create prompts for AI systems, Evaluate content relevance, Generate responses to prompts, Rewrite existing text, Produce concise summaries, Convert spoken language to text, Convert text from one language to another, Gather and compile data for AI models", "domainKnowledge" : "Generative AI; Large Language Models", "domainList" : [ "generative-ai", "large-language-models" ], "domainSpecificKnowledge" : [ "Generative AI", "Large Language Models" ], "qualificationsBasic" : "High School Diploma or higher, Professional or Expert level proficiency (C1/C2) in English", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Data Annotators to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program. This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. What You’ll Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. You’ll Thrive in This Role If You Have: A High School Diploma or higher is required. Professional or Expert level proficiency (C1/C2) in English The expected hourly salary range for this position is $9 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.", "summaryDescription" : "AI Annotation Specialist at Innodata Inc.. Skills: Data Annotation, AI Development, LLM. Rate/assess AI model performance. Label content elements", "postedDate" : "2026-06-14T19:15:23.902", "createdAt" : "2026-06-14T19:15:23.902", "lastUpdated" : "2026-06-15T21:25:40.257", "ingestTimestamp" : "2026-06-14T19:15:23.902", "mongoId" : "6a2c6ace74d8ee00a090e135", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 35000, "salaryAnnualMax" : 50000, "salaryBand" : "30k-50k", "hasSalary" : true, "allSkills" : [ "Data Annotation", "AI Development", "LLM", "Generative AI", "Large Language Models" ], "skillCount" : 5, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 1060.5255, "daysOld" : 1, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2c6ace74d8ee00a090e15e", "title" : "Data Annotation Specialist", "company" : "Innodata Inc.", "industry" : "Technology", "salary" : { "min" : 9.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "ISL - Remote - US", "locations" : [ "ISL - Remote - US" ], "timestamp" : "2026-06-16T00:34:29.192", "fingerprint" : "f0037504820c4319c0dcd5849cdd4b980f6d0d88384743da6b213fee3b834e01", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/innodatainc/jobs/4232528009", "externalId" : "4232528009", "industryFunction" : { }, "structuredLocations" : [ { "country" : "United States", "continent" : "na", "workMode" : "remote" } ], "locationText" : "United States", "employmentType" : "PART_TIME", "minimumEducation" : "High School", "majorSkills" : "Data Annotation, AI Development, LLM", "majorSkillsList" : [ "Data Annotation", "AI Development", "LLM" ], "coreResponsibilities" : "Rate/assess AI model performance, Label content elements, Assign predefined categories or labels, Evaluate content quality and/or appropriateness, Generate labels to advance understanding, Create additional training data, Apply transformations to data, Review data and identify product feature functionality, Label model outputs, Order or rank items, Create prompts or questions for AI, Evaluate content relevance, Generate responses to prompts, Rewrite existing text, Produce concise summaries, Convert spoken language to text, Convert language from one to another, Gather and compile data", "domainKnowledge" : "Generative AI; Large Language Models", "domainList" : [ "generative-ai", "large-language-models" ], "domainSpecificKnowledge" : [ "Generative AI", "Large Language Models" ], "qualificationsBasic" : "High School Diploma or higher, Professional or Expert level proficiency (C1/C2) in English", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Data Annotators to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program. This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. What You’ll Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. You’ll Thrive in This Role If You Have: A High School Diploma or higher is required. Professional or Expert level proficiency (C1/C2) in English The expected hourly salary range for this position is $9 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.", "summaryDescription" : "Data Annotation Specialist at Innodata Inc.. Skills: Data Annotation, AI Development, LLM. Rate/assess AI model performance. Label content elements", "postedDate" : "2026-06-14T19:09:07.533", "createdAt" : "2026-06-14T19:09:07.533", "lastUpdated" : "2026-06-16T00:34:29.192", "ingestTimestamp" : "2026-06-14T19:09:07.533", "mongoId" : "6a2c6ace74d8ee00a090e15e", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 18720, "salaryBand" : "<30k", "hasSalary" : true, "allSkills" : [ "Data Annotation", "AI Development", "LLM", "Generative AI", "Large Language Models" ], "skillCount" : 5, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 1059.9708, "daysOld" : 1, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2c6ace74d8ee00a090e167", "title" : "Generative AI Annotator", "company" : "Innodata Inc.", "industry" : "Technology", "salary" : { "min" : 35000.0, "max" : 55000.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for an entry-level Generative AI Annotator in the US, adjusted for part-time employment." }, "location" : "ISL - Remote - US", "locations" : [ "ISL - Remote - US" ], "timestamp" : "2026-06-16T04:13:40.553", "fingerprint" : "ecce8e2d6247269633172c3aea797232922a2ae7c0b2d942216d972d01f68732", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/innodatainc/jobs/4234818009", "externalId" : "4234818009", "industryFunction" : { }, "structuredLocations" : [ { "country" : "United States", "continent" : "na", "workMode" : "remote" } ], "locationText" : "United States", "employmentType" : "PART_TIME", "minimumEducation" : "High School", "majorSkills" : "Generative AI, Large Language Models", "majorSkillsList" : [ "Generative AI", "Large Language Models" ], "coreResponsibilities" : "Rate/assess AI model performance, Label content elements, Assign predefined categories or labels, Evaluate content quality/appropriateness, Generate labels to advance understanding, Create training data by applying transformations, Review data and identify product feature function, Label model outputs, Order or rank items, Create prompts for AI systems, Evaluate content relevance, Generate responses to prompts, Rewrite existing text, Produce concise summaries, Convert spoken language to text, Convert text from one language to another, Gather and compile data for AI models", "domainKnowledge" : "Generative AI; Large Language Models", "domainList" : [ "generative-ai", "large-language-models" ], "domainSpecificKnowledge" : [ "Generative AI", "Large Language Models" ], "qualificationsBasic" : "High School Diploma or higher, Professional or Expert level proficiency (C1/C2) in English", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Generative AI Annotator to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program. This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. What You’ll Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. You’ll Thrive in This Role If You Have: A High School Diploma or higher is required. Professional or Expert level proficiency (C1/C2) in English The expected hourly salary range for this position is $9 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.", "summaryDescription" : "Generative AI Annotator at Innodata Inc.. Skills: Generative AI, Large Language Models. Rate/assess AI model performance. Label content elements", "postedDate" : "2026-06-14T21:26:50.654", "createdAt" : "2026-06-14T21:26:50.654", "lastUpdated" : "2026-06-16T04:13:40.553", "ingestTimestamp" : "2026-06-14T21:26:50.654", "mongoId" : "6a2c6ace74d8ee00a090e167", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 35000, "salaryAnnualMax" : 55000, "salaryBand" : "30k-50k", "hasSalary" : true, "allSkills" : [ "Generative AI", "Large Language Models" ], "skillCount" : 2, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 1052.5283, "daysOld" : 1, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2c6ace74d8ee00a090e15a", "title" : "Data Annotator", "company" : "Innodata Inc.", "industry" : "Technology", "salary" : { "min" : 240000.0, "max" : 480000.0, "currency" : "INR", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for an entry-level Data Annotator in India, adjusted for part-time employment." }, "location" : "ISL - Remote - US", "locations" : [ "ISL - Remote - US" ], "timestamp" : "2026-06-15T06:46:26.516", "fingerprint" : "ca9feb2deb00ae1cb491b3dae921b1d080fa94e66bc6163e8e13fecaafe908ea", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/innodatainc/jobs/4235831009", "externalId" : "4235831009", "industryFunction" : { }, "structuredLocations" : [ { "workMode" : "remote" } ], "locationText" : "", "employmentType" : "PART_TIME", "minimumEducation" : "High School", "technicalSkills" : "AI, Generative AI, Large Language Models, Machine Learning", "professionalSkills" : [ "Data annotation", "Content evaluation", "Data labeling", "Data transformation", "Content review", "Data gathering", "Data compilation", "Text summarization", "Speech to text", "Translation", "Prompt creation", "Response generation", "Text rewriting" ], "hardSkillsList" : [ "Language intricacies", "Reasoning" ], "hardSkills" : [ { "name" : "Language intricacies", "level" : "intermediate" }, { "name" : "Reasoning", "level" : "intermediate" } ], "majorSkills" : "Data annotation, AI development", "majorSkillsList" : [ "Data annotation", "AI development" ], "coreResponsibilities" : "Rate AI model performance, Assess AI model behavior, Label content elements, Assign predefined categories, Evaluate content quality, Evaluate content appropriateness, Generate labels for concepts, Generate labels for trends, Create training data, Apply data transformations, Modify images, Generate new text, Alter audio/video signals, Review data for product features, Identify product feature function, Label model outputs, Identify content type, Order items by criteria, Rank items by criteria, Create prompts for AI, Generate responses from AI, Rewrite existing text, Preserve original meaning, Improve text clarity, Improve text style, Produce text summaries, Produce data summaries, Convert audio to text, Translate content, Gather digital content, Compile digital content", "domainKnowledge" : "Generative AI; Large Language Models", "domainList" : [ "generative-ai", "large-language-models" ], "domainSpecificKnowledge" : [ "Generative AI", "Large Language Models" ], "qualificationsBasic" : "High School Diploma or higher, Professional or Expert level proficiency in English", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Data Annotators to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program. This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. What You’ll Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. You’ll Thrive in This Role If You Have: A High School Diploma or higher is required. Professional or Expert level proficiency (C1/C2) in English The expected hourly salary range for this position is $13 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.", "summaryDescription" : "Data Annotator at Innodata Inc.. Skills: Data annotation, AI development. Rate AI model performance. Assess AI model behavior", "postedDate" : "2026-06-13T20:43:22.195", "createdAt" : "2026-06-13T20:43:22.195", "lastUpdated" : "2026-06-15T06:46:26.516", "ingestTimestamp" : "2026-06-13T20:43:22.195", "mongoId" : "6a2c6ace74d8ee00a090e15a", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryBand" : "200k+", "hasSalary" : false, "allSkills" : [ "Language intricacies", "Reasoning", "Data annotation", "AI development", "Content evaluation", "Data labeling", "Data transformation", "Content review", "Data gathering", "Data compilation", "Text summarization", "Speech to text", "Translation", "Prompt creation", "Response generation", "Text rewriting", "Generative AI", "Large Language Models" ], "skillCount" : 18, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 1048.3333, "daysOld" : 1, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2ca05474d8ee00a09a1e59", "title" : "Data Annotation Specialist", "company" : "Saronic Technologies", "industry" : "Software", "salary" : { "min" : 42000.0, "max" : 58000.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for an Entry-level Data Annotation Specialist in Austin, United States, adjusted for the technology industry and contract employment type." }, "location" : "Austin, TX", "locations" : [ "Austin, TX" ], "timestamp" : "2026-06-16T02:28:21.795", "fingerprint" : "86d6befb0b593bae139fe66e3e03918ebb395121850177663e1c5ae2e77a5e83", "source" : "ashby", "language" : "en", "applyUrl" : "https://jobs.ashbyhq.com/saronic/f418b71d-d2e1-49e3-9a54-b41a2c5f5e97/application", "externalId" : "f418b71d-d2e1-49e3-9a54-b41a2c5f5e97", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Austin", "state" : "Texas", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "30.2711286,-97.7436995" } ], "locationText" : "Austin, Texas, United States", "employmentType" : "CONTRACT", "technicalSkills" : "Computer vision", "hardSkillsList" : [ "Data annotation", "Image annotation", "Video annotation", "Sensor data annotation", "Object identification", "Quality review", "Labeling guidelines" ], "hardSkills" : [ { "name" : "Data annotation", "level" : "intermediate" }, { "name" : "Image annotation", "level" : "intermediate" }, { "name" : "Video annotation", "level" : "intermediate" }, { "name" : "Sensor data annotation", "level" : "intermediate" }, { "name" : "Object identification", "level" : "intermediate" }, { "name" : "Quality review", "level" : "intermediate" }, { "name" : "Labeling guidelines", "level" : "intermediate" } ], "majorSkills" : "Data annotation, Computer vision", "majorSkillsList" : [ "Data annotation", "Computer vision" ], "coreResponsibilities" : "Annotate visual datasets, Review visual datasets, Train machine-learning models, Evaluate machine-learning models, Annotate image data, Annotate video data, Annotate infrared data, Annotate sensor data, Identify vessels, Identify objects, Identify environmental features, Maintain accuracy, Maintain consistency, Maintain productivity, Apply labeling guidelines, Escalate unclear edge cases, Perform manual annotation, Review auto-labeled data, Support priority project deadlines", "teamCollaboration" : "Support technical team", "domainKnowledge" : "Maritime autonomy; Maritime perception", "domainList" : [ "maritime-autonomy", "maritime-perception" ], "domainSpecificKnowledge" : [ "Maritime autonomy", "Maritime perception" ], "toolProficiency" : [ "Labelbox", "CVAT" ], "qualificationsBasic" : "Prior experience in computer vision data annotation, Familiarity with annotation tools, Experience with annotation types, Visual pattern recognition, Spatial reasoning, Attention to detail, Comfortable performing repetitive work, Able to adapt to changing priorities, Communication skills, Willingness to ask questions, Willingness to accept feedback, Willingness to collaborate with the team", "qualificationsPreferred" : "Basic understanding of maritime environments, Basic understanding of autonomous systems, Basic understanding of robotics, Basic understanding of defense technology", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "Saronic Technologies is a leader in revolutionizing autonomy at sea, dedicated to developing state-of-the-art solutions that enhance maritime operations through autonomous and intelligent platforms. JOB OVERVIEW We are seeking a Data Annotation Specialist to annotate and review visual datasets used to train and evaluate machine-learning models for maritime perception and autonomy. This role supports our software, perception, and autonomy teams by ensuring labeled data is accurate, consistent, and useful for model development. The ideal candidate has prior computer vision annotation experience, strong visual attention to detail, and the ability to maintain speed and accuracy through repetitive labeling work. This person should be comfortable following detailed instructions, adapting as labeling rules change, and supporting a fast-moving technical team. This is an on-site, full-time contract role with an intended path to full-time conversion based on performance and business needs. Upon conversion, the employee would be eligible for Saronic’s standard full-time benefits. This position reports to the Data Annotation Manager. RESPONSIBILITIES - Annotate and review large volumes of image, video, infrared, and other sensor data using computer vision labeling methods. - Identify vessels, objects, environmental features, and other elements relevant to maritime autonomy. - Maintain accuracy, consistency, and productivity across repetitive, detail-heavy datasets. - Apply evolving labeling guidelines and escalate unclear edge cases when needed. - Perform both manual annotation work and quality review of auto-labeled data as needed. - Willingness to support priority project deadlines when needed. QUALIFICATIONS - Prior experience in computer vision data annotation or labeling. - Familiarity with annotation tools such as Labelbox, CVAT, or similar - Experience with annotation types such as segmentation masks, bounding boxes, key points, object tracking, or classification. - Strong visual pattern recognition, spatial reasoning, and attention to detail. - Comfortable performing repetitive, process-driven work for extended periods while maintaining quality. - Able to adapt to changing project priorities, labeling rules, and quality standards in a fast-paced environment. - Strong communication skills and willingness to ask questions, accept feedback, and collaborate with the team. - Basic understanding of maritime environments, autonomous systems, robotics, or defense technology is a plus. Saronic CCPA Notice for Candidates and California Employees https://saronic-assets-public.s3.us-east-2.amazonaws.com/Saronic%20CCPA%20Notice%20for%20Candidates%20and%20California%20Employees.pdf If this role is based in the United States, it requires access to export-controlled information or items that require “U.S. Person” status. As defined by U.S. law, individuals who are any one of the following are considered to be a “U.S. Person”: (1) U.S. citizens, (2) legal permanent residents (a.k.a. green card holders), and (3) certain protected classes of asylees and refugees, as defined in 8 U.S.C. 1324b(a)(3) https://www.govinfo.gov/link/uscode/8/1324b. Saronic does not discriminate on the basis of race, sex, color, religion, age, national origin, marital status, disability, veteran status, genetic information, sexual orientation, gender identity or any other reason prohibited by law in provision of employment opportunities and benefits. We are also committed to providing reasonable accommodations for qualified individuals with disabilities.", "summaryDescription" : "Data Annotation Specialist at Saronic Technologies. Skills: Data annotation, Computer vision. Annotate visual datasets. Review visual datasets", "createdAt" : "2026-06-14T18:14:23.473", "lastUpdated" : "2026-06-16T02:28:21.795", "ingestTimestamp" : "2026-06-14T18:14:23.473", "mongoId" : "6a2ca05474d8ee00a09a1e59", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 42000, "salaryAnnualMax" : 58000, "salaryBand" : "30k-50k", "hasSalary" : true, "allSkills" : [ "Data annotation", "Image annotation", "Video annotation", "Sensor data annotation", "Object identification", "Quality review", "Labeling guidelines", "Computer vision", "Maritime autonomy", "Maritime perception", "Labelbox", "CVAT" ], "skillCount" : 12, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.7777778, "_score" : 1046.7775, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2c6ace74d8ee00a090e156", "title" : "Data Annotator", "company" : "Innodata Inc.", "industry" : "Technology", "salary" : { "min" : 200000.0, "max" : 400000.0, "currency" : "INR", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for an entry-level Data Annotator in India, adjusted for part-time employment." }, "location" : "ISL - Remote - US", "locations" : [ "ISL - Remote - US" ], "timestamp" : "2026-06-15T11:07:30.384", "fingerprint" : "c2fc1fdc7bc454b442c6fe16fe6bb4624209a7515a4285a5798d21e39a48d3dc", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/innodatainc/jobs/4235827009", "externalId" : "4235827009", "industryFunction" : { }, "structuredLocations" : [ { "workMode" : "remote" } ], "locationText" : "", "employmentType" : "PART_TIME", "minimumEducation" : "High School", "technicalSkills" : "AI, Generative AI, Large Language Models, Machine Learning", "professionalSkills" : [ "Data engineering", "Evaluation frameworks", "Human expertise" ], "hardSkillsList" : [ "Data annotation", "Content labeling", "Data transformation", "Data gathering", "Data compilation", "Text summarization", "Speech to text", "Translation" ], "hardSkills" : [ { "name" : "Data annotation", "level" : "intermediate" }, { "name" : "Content labeling", "level" : "intermediate" }, { "name" : "Data transformation", "level" : "intermediate" }, { "name" : "Data gathering", "level" : "intermediate" }, { "name" : "Data compilation", "level" : "intermediate" }, { "name" : "Text summarization", "level" : "intermediate" }, { "name" : "Speech to text", "level" : "intermediate" }, { "name" : "Translation", "level" : "intermediate" } ], "majorSkills" : "Data annotation, AI development, Language understanding", "majorSkillsList" : [ "Data annotation", "AI development", "Language understanding" ], "coreResponsibilities" : "Rate AI model performance, Assess AI model behavior, Label content elements, Assign predefined categories, Evaluate content quality, Evaluate content appropriateness, Generate labels for concepts, Generate labels for trends, Create training data, Apply data transformations, Modify images, Generate new text, Alter audio/video signals, Review data, Identify product feature function, Label model outputs, Identify content presence, Order items, Rank items, Create prompts for AI, Generate responses from AI, Rewrite existing text, Preserve original meaning, Produce concise summaries, Convert audio to text, Convert language to text, Gather data, Compile data", "domainKnowledge" : "Generative AI; Large Language Models", "domainList" : [ "generative-ai", "large-language-models" ], "domainSpecificKnowledge" : [ "Generative AI", "Large Language Models" ], "outcomesObjectives" : "Build AI systems at scale; Advance AI responsibly", "qualificationsBasic" : "High School Diploma or higher, Professional or Expert level proficiency in English", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Data Annotators to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program. This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. What You’ll Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. You’ll Thrive in This Role If You Have: A High School Diploma or higher is required. Professional or Expert level proficiency (C1/C2) in English The expected hourly salary range for this position is $13 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.", "summaryDescription" : "Data Annotator at Innodata Inc.. Skills: Data annotation, AI development, Language understanding. Rate AI model performance. Assess AI model behavior", "postedDate" : "2026-06-13T20:08:26.994", "createdAt" : "2026-06-13T20:08:26.994", "lastUpdated" : "2026-06-15T11:07:30.384", "ingestTimestamp" : "2026-06-13T20:08:26.994", "mongoId" : "6a2c6ace74d8ee00a090e156", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryBand" : "200k+", "hasSalary" : false, "allSkills" : [ "Data annotation", "Content labeling", "Data transformation", "Data gathering", "Data compilation", "Text summarization", "Speech to text", "Translation", "AI development", "Language understanding", "Data engineering", "Evaluation frameworks", "Human expertise", "Generative AI", "Large Language Models" ], "skillCount" : 15, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 1043.1532, "daysOld" : 1, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2c6ace74d8ee00a090e139", "title" : "AI Annotator", "company" : "Innodata Inc.", "industry" : "Technology", "salary" : { "min" : 9.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "ISL - Remote - US", "locations" : [ "ISL - Remote - US" ], "timestamp" : "2026-06-15T07:38:36.687", "fingerprint" : "6427be849ee79907c7159fbf378fbf7b7a9faca428ed3e96a6edeb31ace20a36", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/innodatainc/jobs/4232491009", "externalId" : "4232491009", "industryFunction" : { }, "structuredLocations" : [ { "country" : "United States", "continent" : "na", "workMode" : "remote" } ], "locationText" : "United States", "employmentType" : "PART_TIME", "minimumEducation" : "High School", "technicalSkills" : "AI, LLMs, Machine learning", "professionalSkills" : [ "Data annotation", "Content evaluation", "Data labeling", "Quality assessment", "Data generation", "Data transformation", "Data review", "Data ranking", "Prompt creation", "Content relevance", "Response generation", "Text rewriting", "Summarization", "Transcription", "Translation", "Data gathering", "Data compilation" ], "hardSkillsList" : [ "Language intricacies", "Reasoning" ], "hardSkills" : [ { "name" : "Language intricacies", "level" : "intermediate" }, { "name" : "Reasoning", "level" : "intermediate" } ], "majorSkills" : "Generative AI, Large language models", "majorSkillsList" : [ "Generative AI", "Large language models" ], "coreResponsibilities" : "Rate AI model performance, Assess AI model behavior, Label content elements, Assign predefined categories, Evaluate content quality, Evaluate content appropriateness, Generate labels for concepts, Create training data, Apply data transformations, Modify images, Generate new text, Alter audio/video signals, Review data, Identify product feature function, Label model outputs, Identify content type, Order items by criteria, Rank items by criteria, Create prompts for AI, Evaluate content relevance, Generate responses from AI, Rewrite existing text, Preserve original meaning, Produce concise summaries, Convert audio to text, Convert language to text, Gather data, Compile data", "domainKnowledge" : "Generative AI; Large language models", "domainList" : [ "generative-ai", "large-language-models" ], "domainSpecificKnowledge" : [ "Generative AI", "Large language models" ], "qualificationsBasic" : "High School Diploma or higher, Professional or Expert level proficiency in English", "qualificationsPreferred" : "PhD preferred, Specific ML framework experience, Cloud platform certs", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Data Annotators to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program. This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. What You’ll Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. You’ll Thrive in This Role If You Have: A High School Diploma or higher is required. Professional or Expert level proficiency (C1/C2) in English The expected hourly salary range for this position is $9 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.", "summaryDescription" : "AI Annotator at Innodata Inc.. Skills: Generative AI, Large language models. Rate AI model performance. Assess AI model behavior", "postedDate" : "2026-06-13T20:08:23.309", "createdAt" : "2026-06-13T20:08:23.309", "lastUpdated" : "2026-06-15T07:38:36.687", "ingestTimestamp" : "2026-06-13T20:08:23.309", "mongoId" : "6a2c6ace74d8ee00a090e139", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 18720, "salaryBand" : "<30k", "hasSalary" : true, "allSkills" : [ "Language intricacies", "Reasoning", "Generative AI", "Large language models", "Data annotation", "Content evaluation", "Data labeling", "Quality assessment", "Data generation", "Data transformation", "Data review", "Data ranking", "Prompt creation", "Content relevance", "Response generation", "Text rewriting", "Summarization", "Transcription", "Translation", "Data gathering", "Data compilation" ], "skillCount" : 21, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 1026.9769, "daysOld" : 1, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2c6ace74d8ee00a090e133", "title" : "AI Annotation Specialist", "company" : "Innodata Inc.", "industry" : "Technology", "salary" : { "min" : 35000.0, "max" : 50000.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for Entry-level AI Annotation Specialist in the US, adjusted for part-time employment." }, "location" : "ISL - Remote - US", "locations" : [ "ISL - Remote - US" ], "timestamp" : "2026-06-15T06:46:01.277", "fingerprint" : "20e504cbb3175f7c029119e0ec046554440f591ae739a4645f2e1750b04d0786", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/innodatainc/jobs/4234805009", "externalId" : "4234805009", "industryFunction" : { }, "structuredLocations" : [ { "country" : "United States", "continent" : "na", "workMode" : "remote" } ], "locationText" : "United States", "employmentType" : "PART_TIME", "minimumEducation" : "High School", "technicalSkills" : "AI, Machine learning, Generative AI, Large language models, LLMs", "professionalSkills" : [ "Data engineering", "Evaluation frameworks", "Human expertise" ], "hardSkillsList" : [ "Data annotation", "Content labeling", "Data transformation", "Data review", "Data generation", "Prompt creation", "Response generation", "Text summarization", "Speech to text", "Translation", "Data gathering", "Data compilation" ], "hardSkills" : [ { "name" : "Data annotation", "level" : "intermediate" }, { "name" : "Content labeling", "level" : "intermediate" }, { "name" : "Data transformation", "level" : "intermediate" }, { "name" : "Data review", "level" : "intermediate" }, { "name" : "Data generation", "level" : "intermediate" }, { "name" : "Prompt creation", "level" : "intermediate" }, { "name" : "Response generation", "level" : "intermediate" }, { "name" : "Text summarization", "level" : "intermediate" }, { "name" : "Speech to text", "level" : "intermediate" }, { "name" : "Translation", "level" : "intermediate" }, { "name" : "Data gathering", "level" : "intermediate" }, { "name" : "Data compilation", "level" : "intermediate" } ], "majorSkills" : "AI Annotation, LLM Development", "majorSkillsList" : [ "AI Annotation", "LLM Development" ], "coreResponsibilities" : "Rate/assess AI model performance, Label content elements, Assign predefined categories/labels, Evaluate content quality/appropriateness, Generate labels for concepts, Create training data for ML models, Apply transformations to data, Review data for product feature function, Label model outputs, Identify branded content, Order or rank items, Create prompts for AI systems, Evaluate content relevancy, Generate responses to prompts, Rewrite existing text, Produce concise summaries, Convert spoken language to text, Convert content between languages, Gather and compile data for AI models", "domainKnowledge" : "Generative AI; Large language models; LLMs", "domainList" : [ "generative-ai", "large-language-models", "llms" ], "domainSpecificKnowledge" : [ "Generative AI", "Large language models", "LLMs" ], "outcomesObjectives" : "Build AI systems that can be trusted at scale; Advance the responsible advancement of artificial intelligence", "qualificationsBasic" : "High School Diploma or higher, Professional or Expert level proficiency (C1/C2) in English", "remoteFriendly" : true, "workSiteFlexibility" : "Flexible", "rawDescription" : "Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious AI Annotation Specialists to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program. This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. What You’ll Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. You’ll Thrive in This Role If You Have: A High School Diploma or higher is required. Professional or Expert level proficiency (C1/C2) in English The expected hourly salary range for this position is $9 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.", "summaryDescription" : "AI Annotation Specialist at Innodata Inc.. Skills: AI Annotation, LLM Development. Rate/assess AI model performance. Label content elements", "postedDate" : "2026-06-14T09:31:18.177", "createdAt" : "2026-06-14T09:31:18.177", "lastUpdated" : "2026-06-15T06:46:01.277", "ingestTimestamp" : "2026-06-14T09:31:18.177", "mongoId" : "6a2c6ace74d8ee00a090e133", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 35000, "salaryAnnualMax" : 50000, "salaryBand" : "30k-50k", "hasSalary" : true, "allSkills" : [ "Data annotation", "Content labeling", "Data transformation", "Data review", "Data generation", "Prompt creation", "Response generation", "Text summarization", "Speech to text", "Translation", "Data gathering", "Data compilation", "AI Annotation", "LLM Development", "Data engineering", "Evaluation frameworks", "Human expertise", "Generative AI", "Large language models", "LLMs" ], "skillCount" : 20, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 1009.5615, "daysOld" : 0, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2c6ace74d8ee00a090e168", "title" : "Generative AI Annotator", "company" : "Innodata Inc.", "industry" : "Technology", "salary" : { "min" : 9.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "ISL - Remote - US", "locations" : [ "ISL - Remote - US" ], "timestamp" : "2026-06-15T08:39:22.482", "fingerprint" : "f43ea4a1f6b8015f6bd1d9c192b9cd07d5458d4fe72a888af93d6874ae28bf7a", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/innodatainc/jobs/4234820009", "externalId" : "4234820009", "industryFunction" : { }, "structuredLocations" : [ { "country" : "United States", "continent" : "na", "workMode" : "remote" } ], "locationText" : "United States", "employmentType" : "PART_TIME", "minimumEducation" : "High School", "technicalSkills" : "Generative AI, Large Language Models, Machine Learning", "hardSkillsList" : [ "Data labeling", "Data transformation", "Content evaluation", "Prompt generation", "Text summarization", "Speech to text", "Translation", "Data gathering" ], "hardSkills" : [ { "name" : "Data labeling", "level" : "intermediate" }, { "name" : "Data transformation", "level" : "intermediate" }, { "name" : "Content evaluation", "level" : "intermediate" }, { "name" : "Prompt generation", "level" : "intermediate" }, { "name" : "Text summarization", "level" : "intermediate" }, { "name" : "Speech to text", "level" : "intermediate" }, { "name" : "Translation", "level" : "intermediate" }, { "name" : "Data gathering", "level" : "intermediate" } ], "majorSkills" : "Generative AI, LLM, Data annotation", "majorSkillsList" : [ "Generative AI", "LLM", "Data annotation" ], "coreResponsibilities" : "Rate/assess AI model performance, Label content elements, Assign predefined categories or labels, Evaluate content quality/appropriateness, Generate labels for concept understanding, Create training data by applying transformations, Review data and identify product feature function, Label model outputs, Order or rank items, Create prompts for AI systems, Evaluate content relevance, Generate responses to prompts, Rewrite existing text, Produce concise summaries, Convert spoken language to text, Convert language from one to another, Gather and compile data", "domainKnowledge" : "Generative AI; Large Language Models", "domainList" : [ "generative-ai", "large-language-models" ], "domainSpecificKnowledge" : [ "Generative AI", "Large Language Models" ], "qualificationsBasic" : "High School Diploma or higher, Professional or Expert level proficiency (C1/C2) in English", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Generative AI Annotator to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program. This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. What You’ll Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. You’ll Thrive in This Role If You Have: A High School Diploma or higher is required. Professional or Expert level proficiency (C1/C2) in English The expected hourly salary range for this position is $9 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.", "summaryDescription" : "Generative AI Annotator at Innodata Inc.. Skills: Generative AI, LLM, Data annotation. Rate/assess AI model performance. Label content elements", "postedDate" : "2026-06-14T09:31:05.906", "createdAt" : "2026-06-14T09:31:05.906", "lastUpdated" : "2026-06-15T08:39:22.482", "ingestTimestamp" : "2026-06-14T09:31:05.906", "mongoId" : "6a2c6ace74d8ee00a090e168", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 18720, "salaryBand" : "<30k", "hasSalary" : true, "allSkills" : [ "Data labeling", "Data transformation", "Content evaluation", "Prompt generation", "Text summarization", "Speech to text", "Translation", "Data gathering", "Generative AI", "LLM", "Data annotation", "Large Language Models" ], "skillCount" : 12, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 1008.62476, "daysOld" : 0, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2c6ace74d8ee00a090e15c", "title" : "Data Annotation Specialist", "company" : "Innodata Inc.", "industry" : "Technology", "salary" : { "min" : 31000.0, "max" : 45000.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for an entry-level Data Annotation Specialist in the US, adjusted for part-time employment." }, "location" : "ISL - Remote - US", "locations" : [ "ISL - Remote - US" ], "timestamp" : "2026-06-15T05:05:47.945", "fingerprint" : "6ae7284a99bb129de52256554a1397590a9b634cdf46c243c80a2b3bfef16778", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/innodatainc/jobs/4232498009", "externalId" : "4232498009", "industryFunction" : { }, "structuredLocations" : [ { "country" : "United States", "continent" : "na", "workMode" : "remote" } ], "locationText" : "United States", "employmentType" : "PART_TIME", "minimumEducation" : "High School", "technicalSkills" : "AI, Generative AI, Large Language Models, Machine Learning", "hardSkillsList" : [ "Data annotation", "Content evaluation", "Data transformation", "Text summarization", "Speech to text", "Translation", "Data gathering", "Prompt creation", "Content relevance evaluation" ], "hardSkills" : [ { "name" : "Data annotation", "level" : "intermediate" }, { "name" : "Content evaluation", "level" : "intermediate" }, { "name" : "Data transformation", "level" : "intermediate" }, { "name" : "Text summarization", "level" : "intermediate" }, { "name" : "Speech to text", "level" : "intermediate" }, { "name" : "Translation", "level" : "intermediate" }, { "name" : "Data gathering", "level" : "intermediate" }, { "name" : "Prompt creation", "level" : "intermediate" }, { "name" : "Content relevance evaluation", "level" : "intermediate" } ], "majorSkills" : "Data annotation, AI development, LLM training", "majorSkillsList" : [ "Data annotation", "AI development", "LLM training" ], "coreResponsibilities" : "Rate/assess AI model performance, Label content elements, Assign predefined categories/labels, Evaluate content quality/appropriateness, Generate labels for concepts/trends, Create training data via transformations, Review data for product feature function, Label model outputs, Order/rank items by criteria, Create prompts for AI systems, Evaluate content relevance, Generate responses from AI systems, Rewrite text preserving meaning, Produce concise summaries, Convert audio to text, Convert language between languages, Gather and compile data", "domainKnowledge" : "Generative AI; Large Language Models", "domainList" : [ "generative-ai", "large-language-models" ], "domainSpecificKnowledge" : [ "Generative AI", "Large Language Models" ], "qualificationsBasic" : "High School Diploma or higher, Professional or Expert level proficiency (C1/C2) in English", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Data Annotation Specialists to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program. This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. What You’ll Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. You’ll Thrive in This Role If You Have: A High School Diploma or higher is required. Professional or Expert level proficiency (C1/C2) in English The expected hourly salary range for this position is $9 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.", "summaryDescription" : "Data Annotation Specialist at Innodata Inc.. Skills: Data annotation, AI development, LLM training. Rate/assess AI model performance. Label content elements", "postedDate" : "2026-06-14T07:55:31.054", "createdAt" : "2026-06-14T07:55:31.054", "lastUpdated" : "2026-06-15T05:05:47.945", "ingestTimestamp" : "2026-06-14T07:55:31.054", "mongoId" : "6a2c6ace74d8ee00a090e15c", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 31000, "salaryAnnualMax" : 45000, "salaryBand" : "30k-50k", "hasSalary" : true, "allSkills" : [ "Data annotation", "Content evaluation", "Data transformation", "Text summarization", "Speech to text", "Translation", "Data gathering", "Prompt creation", "Content relevance evaluation", "AI development", "LLM training", "Generative AI", "Large Language Models" ], "skillCount" : 13, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 999.22003, "daysOld" : 0, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2c6ace74d8ee00a090e166", "title" : "Generative AI Annotator", "company" : "Innodata Inc.", "industry" : "Technology", "salary" : { "min" : 9.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "ISL - Remote - US", "locations" : [ "ISL - Remote - US" ], "timestamp" : "2026-06-15T10:26:41.493", "fingerprint" : "f21df527bfa9586cbe0b0249f3ce5fb5e9176e8c153334ea73f4c2d0a96e4ee7", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/innodatainc/jobs/4234815009", "externalId" : "4234815009", "industryFunction" : { }, "structuredLocations" : [ { "country" : "United States", "continent" : "na", "workMode" : "remote" } ], "locationText" : "United States", "employmentType" : "PART_TIME", "minimumEducation" : "High School", "technicalSkills" : "Generative AI, Large Language Models, AI models, Machine learning models", "professionalSkills" : [ "Data engineering" ], "hardSkillsList" : [ "Data labeling", "Content evaluation", "Data transformation", "Data gathering", "Data compilation", "Text summarization", "Speech to text", "Translation" ], "hardSkills" : [ { "name" : "Data labeling", "level" : "intermediate" }, { "name" : "Content evaluation", "level" : "intermediate" }, { "name" : "Data transformation", "level" : "intermediate" }, { "name" : "Data gathering", "level" : "intermediate" }, { "name" : "Data compilation", "level" : "intermediate" }, { "name" : "Text summarization", "level" : "intermediate" }, { "name" : "Speech to text", "level" : "intermediate" }, { "name" : "Translation", "level" : "intermediate" } ], "majorSkills" : "Generative AI, LLM development", "majorSkillsList" : [ "Generative AI", "LLM development" ], "coreResponsibilities" : "Rate/assess AI model performance, Label content elements, Assign predefined categories, Evaluate content quality, Generate labels for concepts, Create training data, Modify images, Generate new text, Alter audio/video signals, Review data for product features, Label model outputs, Identify content types, Order or rank items, Create prompts for AI, Evaluate content relevance, Generate responses to prompts, Rewrite existing text, Produce text summaries, Convert audio to text, Translate content, Gather and compile data", "teamCollaboration" : "Global contributor community; SME on Demand program", "domainKnowledge" : "Generative AI; Large Language Models", "domainList" : [ "generative-ai", "large-language-models" ], "domainSpecificKnowledge" : [ "Generative AI", "Large Language Models" ], "qualificationsBasic" : "High School Diploma or higher, Professional or Expert level proficiency (C1/C2) in English", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Generative AI Annotator to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program. This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. What You’ll Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. You’ll Thrive in This Role If You Have: A High School Diploma or higher is required. Professional or Expert level proficiency (C1/C2) in English The expected hourly salary range for this position is $9 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.", "summaryDescription" : "Generative AI Annotator at Innodata Inc.. Skills: Generative AI, LLM development. Rate/assess AI model performance. Label content elements", "postedDate" : "2026-06-14T09:25:50.192", "createdAt" : "2026-06-14T09:25:50.192", "lastUpdated" : "2026-06-15T10:26:41.493", "ingestTimestamp" : "2026-06-14T09:25:50.192", "mongoId" : "6a2c6ace74d8ee00a090e166", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 18720, "salaryBand" : "<30k", "hasSalary" : true, "allSkills" : [ "Data labeling", "Content evaluation", "Data transformation", "Data gathering", "Data compilation", "Text summarization", "Speech to text", "Translation", "Generative AI", "LLM development", "Data engineering", "Large Language Models" ], "skillCount" : 12, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 990.7615, "daysOld" : 1, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2c6ace74d8ee00a090e165", "title" : "Generative AI Annotator", "company" : "Innodata Inc.", "industry" : "Technology", "salary" : { "min" : 31000.0, "max" : 45000.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for entry-level Generative AI Annotator in the US, adjusted for part-time employment." }, "location" : "ISL - Remote - US", "locations" : [ "ISL - Remote - US" ], "timestamp" : "2026-06-15T13:05:49.125", "fingerprint" : "f3e3bf60d2ecf78e8f14b4d52e38f18d3cdd8c18696b26a42bfeb3df6fd2534a", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/innodatainc/jobs/4234813009", "externalId" : "4234813009", "industryFunction" : { }, "structuredLocations" : [ { "country" : "United States", "continent" : "na", "workMode" : "remote" } ], "locationText" : "United States", "employmentType" : "PART_TIME", "minimumEducation" : "High School", "technicalSkills" : "Generative AI, Large language models, Machine learning models, AI algorithms, Machine learning", "professionalSkills" : [ "Data engineering", "Evaluation frameworks", "Human expertise", "Data quality", "Outcome delivery" ], "hardSkillsList" : [ "Data labeling", "Content evaluation", "Data transformation", "Data review", "Data ordering", "Prompt creation", "Content relevance", "Response generation", "Text rewriting", "Summarization", "Transcription", "Translation", "Data gathering", "Data compilation" ], "hardSkills" : [ { "name" : "Data labeling", "level" : "intermediate" }, { "name" : "Content evaluation", "level" : "intermediate" }, { "name" : "Data transformation", "level" : "intermediate" }, { "name" : "Data review", "level" : "intermediate" }, { "name" : "Data ordering", "level" : "intermediate" }, { "name" : "Prompt creation", "level" : "intermediate" }, { "name" : "Content relevance", "level" : "intermediate" }, { "name" : "Response generation", "level" : "intermediate" }, { "name" : "Text rewriting", "level" : "intermediate" }, { "name" : "Summarization", "level" : "intermediate" }, { "name" : "Transcription", "level" : "intermediate" }, { "name" : "Translation", "level" : "intermediate" }, { "name" : "Data gathering", "level" : "intermediate" }, { "name" : "Data compilation", "level" : "intermediate" } ], "majorSkills" : "Generative AI, LLM annotation, Data labeling", "majorSkillsList" : [ "Generative AI", "LLM annotation", "Data labeling" ], "coreResponsibilities" : "Rate AI model performance, Assess AI algorithm performance, Label content elements, Assign predefined categories, Evaluate content quality, Evaluate content appropriateness, Generate labels, Create training data, Apply data transformations, Modify images, Generate new text, Alter audio/video signals, Review data, Identify product feature function, Label model outputs, Identify content type, Order items, Rank items, Create prompts, Generate responses, Rewrite existing text, Preserve original meaning, Produce concise summaries, Convert audio to text, Convert text to another language, Gather data, Compile data", "domainKnowledge" : "Generative AI; Large language models; AI systems", "domainList" : [ "generative-ai", "large-language-models", "ai-systems" ], "domainSpecificKnowledge" : [ "Generative AI", "Large language models", "AI systems" ], "outcomesObjectives" : "Build AI systems at scale; Deliver highest quality data; Deliver outstanding outcomes", "qualificationsBasic" : "High School Diploma or higher, Professional or Expert level proficiency (C1/C2) in English", "qualificationsPreferred" : "PhD preferred, Specific ML framework experience, Cloud platform certs", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Generative AI Annotator to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program. This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. What You’ll Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. You’ll Thrive in This Role If You Have: A High School Diploma or higher is required. Professional or Expert level proficiency (C1/C2) in English The expected hourly salary range for this position is $9 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.", "summaryDescription" : "Generative AI Annotator at Innodata Inc.. Skills: Generative AI, LLM annotation, Data labeling. Rate AI model performance. Assess AI algorithm performance", "postedDate" : "2026-06-14T03:34:55.088", "createdAt" : "2026-06-14T03:34:55.088", "lastUpdated" : "2026-06-15T13:05:49.125", "ingestTimestamp" : "2026-06-14T03:34:55.088", "mongoId" : "6a2c6ace74d8ee00a090e165", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 31000, "salaryAnnualMax" : 45000, "salaryBand" : "30k-50k", "hasSalary" : true, "allSkills" : [ "Data labeling", "Content evaluation", "Data transformation", "Data review", "Data ordering", "Prompt creation", "Content relevance", "Response generation", "Text rewriting", "Summarization", "Transcription", "Translation", "Data gathering", "Data compilation", "Generative AI", "LLM annotation", "Data engineering", "Evaluation frameworks", "Human expertise", "Data quality", "Outcome delivery", "Large language models", "AI systems" ], "skillCount" : 23, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 976.0205, "daysOld" : 1, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2c6ace74d8ee00a090e153", "title" : "Data AI Annotator", "company" : "Innodata Inc.", "industry" : "Technology", "salary" : { "min" : 31000.0, "max" : 45000.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for Entry-level Data AI Annotator in the US, adjusted for remote work and the technology industry." }, "location" : "ISL - Remote - US", "locations" : [ "ISL - Remote - US" ], "timestamp" : "2026-06-15T09:02:51.954", "fingerprint" : "f28a631641bd897018ea514eeb1e755b97e8bb9aaffee61813c50eedd793835e", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/innodatainc/jobs/4266784009", "externalId" : "4266784009", "industryFunction" : { }, "structuredLocations" : [ { "country" : "United States", "continent" : "na", "workMode" : "remote" } ], "locationText" : "United States", "employmentType" : "PART_TIME", "minimumEducation" : "Bachelor's", "technicalSkills" : "Generative AI, Large Language Models, Machine Learning", "professionalSkills" : [ "Data engineering", "AI development" ], "hardSkillsList" : [ "Data labeling", "Content evaluation", "Data transformation", "Prompt creation", "Text summarization", "Translation", "Data gathering", "Data compilation" ], "hardSkills" : [ { "name" : "Data labeling", "level" : "intermediate" }, { "name" : "Content evaluation", "level" : "intermediate" }, { "name" : "Data transformation", "level" : "intermediate" }, { "name" : "Prompt creation", "level" : "intermediate" }, { "name" : "Text summarization", "level" : "intermediate" }, { "name" : "Translation", "level" : "intermediate" }, { "name" : "Data gathering", "level" : "intermediate" }, { "name" : "Data compilation", "level" : "intermediate" } ], "majorSkills" : "Generative AI, LLM development, Data annotation", "majorSkillsList" : [ "Generative AI", "LLM development", "Data annotation" ], "coreResponsibilities" : "Rate AI model performance, Label content elements, Assign predefined categories, Evaluate content quality, Generate labels for concepts, Create training data, Review data for product features, Label model outputs, Order or rank items, Create prompts for AI systems, Evaluate content relevance, Generate responses to prompts, Rewrite existing text, Produce concise summaries, Convert audio to text, Translate content, Gather and compile data", "domainKnowledge" : "Artificial Intelligence; Large Language Models", "domainList" : [ "artificial-intelligence", "large-language-models" ], "domainSpecificKnowledge" : [ "Artificial Intelligence", "Large Language Models" ], "qualificationsBasic" : "Bachelor's degree or higher, Professional or Expert level proficiency in English", "qualificationsPreferred" : "Advanced degrees strongly preferred, Master's or PhD preferred", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Generative AI Specialist to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program. This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. What You’ll Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. You’ll Thrive in This Role If You Have: A Bachelor’s degree or higher in a humanities specialization is required. Advanced degrees are strongly preferred (Master’s or PhD) Professional or Expert level proficiency (C1/C2) in English The expected hourly salary range for this position is $15 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.", "summaryDescription" : "Data AI Annotator at Innodata Inc.. Skills: Generative AI, LLM development, Data annotation. Rate AI model performance. Label content elements", "postedDate" : "2026-06-14T02:56:15.234", "createdAt" : "2026-06-14T02:56:15.234", "lastUpdated" : "2026-06-15T09:02:51.954", "ingestTimestamp" : "2026-06-14T02:56:15.234", "mongoId" : "6a2c6ace74d8ee00a090e153", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 31000, "salaryAnnualMax" : 45000, "salaryBand" : "30k-50k", "hasSalary" : true, "allSkills" : [ "Data labeling", "Content evaluation", "Data transformation", "Prompt creation", "Text summarization", "Translation", "Data gathering", "Data compilation", "Generative AI", "LLM development", "Data annotation", "Data engineering", "AI development", "Artificial Intelligence", "Large Language Models" ], "skillCount" : 15, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 973.4452, "daysOld" : 1, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2c6ace74d8ee00a090e164", "title" : "Generative AI Annotator", "company" : "Innodata Inc.", "industry" : "Technology", "salary" : { "min" : 31200.0, "max" : 46800.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for entry-level remote technology roles in the US, adjusted for part-time employment." }, "location" : "ISL - Remote - US", "locations" : [ "ISL - Remote - US" ], "timestamp" : "2026-06-15T10:19:23.517", "fingerprint" : "4e7ec68659f1197b6a581ecf278a3d5d1acc5ddd3547c56c83fdd670ab8115c8", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/innodatainc/jobs/4232484009", "externalId" : "4232484009", "industryFunction" : { }, "structuredLocations" : [ { "country" : "United States", "continent" : "na", "workMode" : "remote" } ], "locationText" : "United States", "employmentType" : "PART_TIME", "minimumEducation" : "High School", "technicalSkills" : "Generative AI, Large language models, Machine learning", "hardSkillsList" : [ "Data labeling", "Content evaluation", "Data transformation", "Data gathering", "Data compilation" ], "hardSkills" : [ { "name" : "Data labeling", "level" : "intermediate" }, { "name" : "Content evaluation", "level" : "intermediate" }, { "name" : "Data transformation", "level" : "intermediate" }, { "name" : "Data gathering", "level" : "intermediate" }, { "name" : "Data compilation", "level" : "intermediate" } ], "majorSkills" : "Generative AI, Large language models, Data annotation", "majorSkillsList" : [ "Generative AI", "Large language models", "Data annotation" ], "coreResponsibilities" : "Rate/assess AI model performance, Label content elements, Assign predefined categories or labels, Evaluate perceived quality or appropriateness, Generate labels to advance understanding, Create additional training data, Apply transformations to data, Review data and identify feature function, Label model outputs, Order or rank items, Create prompts or questions, Evaluate content relevance, Generate responses to prompts, Rewrite existing text, Produce concise summaries, Convert spoken language to text, Convert content from one language to another, Gather and compile data", "domainKnowledge" : "Artificial intelligence; Large language models", "domainList" : [ "artificial-intelligence", "large-language-models" ], "domainSpecificKnowledge" : [ "Artificial intelligence", "Large language models" ], "qualificationsBasic" : "High School Diploma or higher, Professional or Expert level proficiency (C1/C2) in English", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Data Annotator to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program. This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. What You’ll Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. You’ll Thrive in This Role If You Have: A High School Diploma or higher is required. Professional or Expert level proficiency (C1/C2) in English The expected hourly salary range for this position is $9 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.", "summaryDescription" : "Generative AI Annotator at Innodata Inc.. Skills: Generative AI, Large language models, Data annotation. Rate/assess AI model performance. Label content elements", "postedDate" : "2026-06-13T21:55:44.298", "createdAt" : "2026-06-13T21:55:44.298", "lastUpdated" : "2026-06-15T10:19:23.517", "ingestTimestamp" : "2026-06-13T21:55:44.298", "mongoId" : "6a2c6ace74d8ee00a090e164", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 31200, "salaryAnnualMax" : 46800, "salaryBand" : "30k-50k", "hasSalary" : true, "allSkills" : [ "Data labeling", "Content evaluation", "Data transformation", "Data gathering", "Data compilation", "Generative AI", "Large language models", "Data annotation", "Artificial intelligence" ], "skillCount" : 9, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 945.0584, "daysOld" : 1, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "6a2c6ace74d8ee00a090e137", "title" : "AI Annotation Specialist", "company" : "Innodata Inc.", "industry" : "Technology", "salary" : { "min" : 31000.0, "max" : 45000.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for Entry-level AI Annotation Specialists in the US, adjusted for part-time employment." }, "location" : "ISL - Remote - US", "locations" : [ "ISL - Remote - US" ], "timestamp" : "2026-06-15T03:47:37.663", "fingerprint" : "a0d497d099f6a5738f4d29e68e441254cacf9292b7d872f7c312544a959b9d3d", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.greenhouse.io/innodatainc/jobs/4232733009", "externalId" : "4232733009", "industryFunction" : { }, "structuredLocations" : [ { "country" : "United States", "continent" : "na", "workMode" : "remote" } ], "locationText" : "United States", "employmentType" : "PART_TIME", "minimumEducation" : "High School", "technicalSkills" : "AI, LLMs, Machine learning", "hardSkillsList" : [ "Data labeling", "Content evaluation", "Data transformation", "Data gathering", "Data compilation" ], "hardSkills" : [ { "name" : "Data labeling", "level" : "intermediate" }, { "name" : "Content evaluation", "level" : "intermediate" }, { "name" : "Data transformation", "level" : "intermediate" }, { "name" : "Data gathering", "level" : "intermediate" }, { "name" : "Data compilation", "level" : "intermediate" } ], "majorSkills" : "Data annotation, AI development, LLM training", "majorSkillsList" : [ "Data annotation", "AI development", "LLM training" ], "coreResponsibilities" : "Rate AI model performance, Assess AI model behavior, Label content elements, Assign predefined categories, Evaluate content quality, Evaluate content appropriateness, Generate labels, Create training data, Apply data transformations, Modify images, Generate new text, Alter audio/video signals, Review data, Identify product feature function, Label model outputs, Identify content type, Order items, Rank items, Create prompts, Create questions, Evaluate content relevance, Generate responses, Rewrite existing text, Preserve original meaning, Produce concise summaries, Convert spoken language, Convert audio content, Convert text, Convert spoken language to text, Convert text to another language, Convert spoken language to another language, Gather data, Compile data", "teamCollaboration" : "Global contributor community; Subject Matter Expert on Demand program", "domainKnowledge" : "Generative AI; Large language models", "domainList" : [ "generative-ai", "large-language-models" ], "domainSpecificKnowledge" : [ "Generative AI", "Large language models" ], "qualificationsBasic" : "High School Diploma or higher, Professional or Expert level proficiency in English", "qualificationsPreferred" : "PhD preferred, Specific ML framework experience, Cloud platform certs", "remoteFriendly" : true, "workSiteFlexibility" : "Flexible", "rawDescription" : "Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Data Annotators to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program. This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. What You’ll Own: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. Labeling elements of a piece of content rather than the content as a whole. Assigning predefined categories or labels to items. Evaluating the perceived quality and/or appropriateness of content Generating labels to advance understanding of a concept, trend etc. Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity. Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. Ordering or ranking items based on a set of preferences or criteria. Creating prompts or questions that will be used to generate responses from a language model or other AI system. Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). Generating responses to prompts or questions using a language model or other AI system. Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. Producing concise summaries of longer pieces of text or data. Converting spoken language or audio content into written text. Converting text or spoken language from one language to another. Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. You’ll Thrive in This Role If You Have: A High School Diploma or higher is required. Professional or Expert level proficiency (C1/C2) in English The expected hourly salary range for this position is $9 p/hour, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.", "summaryDescription" : "AI Annotation Specialist at Innodata Inc.. Skills: Data annotation, AI development, LLM training. Rate AI model performance. Assess AI model behavior", "postedDate" : "2026-06-13T21:29:06.790", "createdAt" : "2026-06-13T21:29:06.790", "lastUpdated" : "2026-06-15T03:47:37.663", "ingestTimestamp" : "2026-06-13T21:29:06.790", "mongoId" : "6a2c6ace74d8ee00a090e137", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 31000, "salaryAnnualMax" : 45000, "salaryBand" : "30k-50k", "hasSalary" : true, "allSkills" : [ "Data labeling", "Content evaluation", "Data transformation", "Data gathering", "Data compilation", "Data annotation", "AI development", "LLM training", "Generative AI", "Large language models" ], "skillCount" : 10, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 943.51843, "daysOld" : 1, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.98 }, { "id" : "68c2e420f8b1b0376634c4b4", "title" : "Catalog Specialist, WWRR Science Annotation Team", "company" : "ADCI HYD 13 SEZ", "industry" : "Fulfillment Operations Management, Catalog, Retail", "salary" : { "min" : 850000.0, "max" : 1300000.0, "currency" : "INR", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for a Mid-level Catalog Specialist in Hyderabad, India, adjusted for the E-commerce industry and company size." }, "location" : "Hyderabad, Telangana, IND", "locations" : [ "Hyderabad, Telangana, IND" ], "timestamp" : "2026-06-15T00:52:44.849", "fingerprint" : "ed87536216a796877c5456d69ab5605c062151c382e32b54401827f22e7358a0", "source" : "amazon", "language" : "en", "applyUrl" : "https://account.amazon.com/jobs/3078240/apply", "externalId" : "3c45ddd2-049f-4182-b0ff-b7a5a1c9b951", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Hyderabad", "state" : "Telangana", "country" : "India", "continent" : "as", "source" : "llm", "workMode" : "onsite", "geo" : "17.3605890,78.4740613" } ], "locationText" : "Hyderabad, Telangana, India", "employmentType" : "FULL_TIME", "minimumEducation" : "Bachelor's", "experienceMinYears" : 1, "experienceMaxYears" : 5, "experienceLevel" : "Mid", "technicalSkills" : "Machine Learning, ML models, Classification algorithms", "professionalSkills" : [ "Data annotation", "ML model training", "ML model evaluation" ], "hardSkillsList" : [ "Data annotation", "Classification algorithms" ], "softSkillsList" : [ "Leadership", "Problem-solving skills" ], "hardSkills" : [ { "name" : "Data annotation", "level" : "intermediate" }, { "name" : "Classification algorithms", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Leadership" }, { "name" : "Problem-solving skills" } ], "majorSkills" : "Data annotation, ML models", "majorSkillsList" : [ "Data annotation", "ML models" ], "coreResponsibilities" : "Support Machine Learning (ML) models, Perform data annotation, Train ML models, Evaluate ML models, Support data scientists, Develop classification algorithms, Collaborate with cross-functional teams, Ensure data annotation tasks align with project objectives, Ensure data annotation tasks align with project timelines, Maintain high-quality standards for annotated data, Optimize model performance, Continuously evaluate annotation processes, Improve annotation processes, Enhance efficiency, Enhance accuracy", "teamCollaboration" : "Cross-functional teams; Product teams; Technology teams; Science teams", "projectManagementSkills" : [ "Time management", "Organization" ], "domainKnowledge" : "Reverse logistics processes; Machine learning concepts; ML algorithms", "domainList" : [ "reverse-logistics-processes", "machine-learning-concepts", "ml-algorithms" ], "domainSpecificKnowledge" : [ "Reverse logistics processes", "Machine learning concepts", "ML algorithms" ], "toolProficiency" : [ "Microsoft Office" ], "innovationCreativity" : "Innovate", "problemSolvingSkills" : "Deep-dive on complex problems; Problem-solving skills", "specialRequirements" : "German language proficiency", "outcomesObjectives" : "Delight customers through new experiences", "qualificationsBasic" : "Bachelor's degree, Speak, write, and read fluently in Spanish, Experience with Microsoft Office products and applications, 1+ years of proven experience in data annotation and labeling for ML model training and evaluation, Minimum B2 Language Proficiency required", "qualificationsPreferred" : "Experience working on the MTurk platform for data annotation tasks, Proven experience in data annotation and labeling for ML model training and evaluation, Understanding of data annotation methodologies and tools, Familiarity with Amazon's product and category ecosystem, Previous exposure to machine learning concepts and algorithms, Demonstrated ability to adapt to evolving technologies and methodologies in the ML domain", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "In the Worldwide Returns, ReCommerce & Sustainability (WW RR&S) group at Amazon, we are dedicated to ‘making zero happen’ – zero cost of returns, zero waste, and zero defects – to benefit our customers, company, and environment. We are an agile and inclusive organization that constantly innovates to create long-term value by investing in our people and our planet, not simply focusing on the bottom line. WW R&R includes business, product, operations, data, and software engineering teams, who together manage the lifecycle of returned and damaged products. In WW R&R, you will partner across these teams to help customers discover great deals on quality used, rentals, and open box items; get the most value out of Amazon’s products; improve the customer returns experience; and reduce defects, waste, and cost in reverse logistics processes. You will be a leader, a builder, and an owner, collaborating cross-functionally with technical, operations, and business teams to design scalable and automated solutions to customer problems. Amazon is Earth’s most customer-centric company and in WW R&R, the Earth is our customer too. Come join us and innovate with the Amazon Worldwide Returns, ReCommerce & Sustainability team! We are hiring an experienced Catalog Specialist to help us grow our business in innovative ways. In this role, you will work closely with our product, technology and science teams to support new Machine Learning (ML) models and data science classification algorithm development – all helping to delight our customers through new experiences throughout their Amazon shopping journey. Need candidates in language proficiency in: German Key job responsibilities • Work closely with our product, technology, and science teams to support Machine Learning (ML) models • Perform data annotation required to train and evaluate ML models effectively • Support data scientists in the development of classification algorithms • Collaborate with cross-functional teams to ensure data annotation tasks align with project objectives and timelines • Maintain high-quality standards for annotated data to optimize model performance • Continuously evaluate and improve annotation processes to enhance efficiency and accuracy • Strong analytical skills and the ability to deep-dive on complex problems • Ability to manage multiple simultaneous projects requiring frequent communication, organization/time management and problem-solving skills Basic Qualifications: Bachelor's degree Speak, write, and read fluently in Spanish Experience with Microsoft Office products and applications 1+ years of proven experience in data annotation and labeling for ML model training and evaluation Minimum B2 Language Proficiency required Preferred Qualifications: Experience working on the MTurk platform for data annotation tasks Proven experience in data annotation and labeling for ML model training and evaluation Understanding of data annotation methodologies and tools Familiarity with Amazon's product and category ecosystem Previous exposure to machine learning concepts and algorithms Demonstrated ability to adapt to evolving technologies and methodologies in the ML domain Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.", "summaryDescription" : "Catalog Specialist, WWRR Science Annotation Team at ADCI HYD 13 SEZ. Skills: Data annotation, ML models. Support Machine Learning (ML) models. Perform data annotation", "postedDate" : "2026-06-13T09:44:58.270", "createdAt" : "2026-06-13T09:44:58.270", "lastUpdated" : "2026-06-15T00:52:44.849", "ingestTimestamp" : "2026-06-13T09:44:58.270", "mongoId" : "68c2e420f8b1b0376634c4b4", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "sourceUrl" : "https://account.amazon.com/jobs/3078240/apply", "salaryBand" : "200k+", "hasSalary" : false, "allSkills" : [ "Data annotation", "Classification algorithms", "Leadership", "Problem-solving skills", "ML models", "ML model training", "ML model evaluation", "Time management", "Organization", "Reverse logistics processes", "Machine learning concepts", "ML algorithms", "Microsoft Office" ], "skillCount" : 13, "jobCategory" : "operations", "dataQualityScore" : 1.0, "_score" : 845.9395, "daysOld" : 1, "hasLocation" : true, "hasGeo" : true }, { "id" : "69e1b5d82beec4dd3d1ac3fb", "title" : "Catalog Specialist, WWRR Science Annotation Team", "company" : "Amazon", "industry" : "Fulfillment Operations Management, Catalog, Retail", "salary" : { "min" : 1200000.0, "max" : 1800000.0, "currency" : "INR", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market data for a Mid-level Catalog Specialist in E-Commerce in Hyderabad, India, adjusted for Amazon's company size and industry." }, "location" : "Hyderabad, Telangana, IND", "locations" : [ "Hyderabad, Telangana, IND" ], "timestamp" : "2026-06-15T04:43:01.861", "fingerprint" : "597317e76471f47eb3d28b5c056372f14ed3a9cafd75b76197623ea199a31bf7", "source" : "amazon", "language" : "en", "applyUrl" : "https://account.amazon.com/jobs/10382953/apply", "externalId" : "79163bdb-6071-48f2-8a8a-ba97e691e5cb", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Hyderabad", "state" : "Telangana", "country" : "India", "continent" : "as", "source" : "llm", "workMode" : "onsite", "geo" : "17.3605890,78.4740613" } ], "locationText" : "Hyderabad, Telangana, India", "employmentType" : "FULL_TIME", "technicalSkills" : "Machine Learning, ML models, Classification algorithms", "professionalSkills" : [ "Data annotation", "Annotation processes" ], "hardSkillsList" : [ "Analytical skills", "Problem-solving skills", "Time management skills" ], "hardSkills" : [ { "name" : "Analytical skills", "level" : "intermediate" }, { "name" : "Problem-solving skills", "level" : "intermediate" }, { "name" : "Time management skills", "level" : "intermediate" } ], "majorSkills" : "Data annotation, ML models", "majorSkillsList" : [ "Data annotation", "ML models" ], "coreResponsibilities" : "Work closely with product, technology, and science teams, Perform data annotation required to train and evaluate, Support data scientists in the development of classification, Collaborate with cross-functional teams to ensure data annotation, Maintain high-quality standards for annotated data to optimize, Continuously evaluate and improve annotation processes to enhance", "teamCollaboration" : "Cross-functional teams", "projectManagementSkills" : [ "Manage multiple simultaneous projects" ], "domainKnowledge" : "Reverse logistics processes; ML domain", "domainList" : [ "reverse-logistics-processes", "ml-domain" ], "domainSpecificKnowledge" : [ "Reverse logistics processes", "ML domain" ], "innovationCreativity" : "Innovate with the Amazon Worldwide Returns, ReCommerce & Sustainability team", "problemSolvingSkills" : "Deep-dive on complex problems", "qualificationsBasic" : "Minimum B2 Level certification in German Language, Spanish language proficiency", "qualificationsPreferred" : "Bachelor's degree or above in computer science, computer engineering, or related field, Experience in communicating technically, Proven experience in data annotation and labeling for ML model training and evaluation, Demonstrated ability to adapt to evolving technologies and methodologies in the ML domain", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "In the Worldwide Returns, ReCommerce & Sustainability (WW RR&S) group at Amazon, we are dedicated to ‘making zero happen’ – zero cost of returns, zero waste, and zero defects – to benefit our customers, company, and environment. We are an agile and inclusive organization that constantly innovates to create long-term value by investing in our people and our planet, not simply focusing on the bottom line. WW R&R includes business, product, operations, data, and software engineering teams, who together manage the lifecycle of returned and damaged products. In WW R&R, you will partner across these teams to help customers discover great deals on quality used, rentals, and open box items; get the most value out of Amazon’s products; improve the customer returns experience; and reduce defects, waste, and cost in reverse logistics processes. You will be a leader, a builder, and an owner, collaborating cross-functionally with technical, operations, and business teams to design scalable and automated solutions to customer problems. Amazon is Earth’s most customer-centric company and in WW R&R, the Earth is our customer too. Come join us and innovate with the Amazon Worldwide Returns, ReCommerce & Sustainability team! We are hiring an experienced Catalog Specialist to help us grow our business in innovative ways. In this role, you will work closely with our product, technology and science teams to support new Machine Learning (ML) models and data science classification algorithm development – all helping to delight our customers through new experiences throughout their Amazon shopping journey. Need candidates in language proficiency in: Spanish Key job responsibilities • Work closely with our product, technology, and science teams to support Machine Learning (ML) models • Perform data annotation required to train and evaluate ML models effectively • Support data scientists in the development of classification algorithms • Collaborate with cross-functional teams to ensure data annotation tasks align with project objectives and timelines • Maintain high-quality standards for annotated data to optimize model performance • Continuously evaluate and improve annotation processes to enhance efficiency and accuracy • Strong analytical skills and the ability to deep-dive on complex problems • Ability to manage multiple simultaneous projects requiring frequent communication, organization/time management and problem-solving skills Basic Qualifications: - Basic qualifications - Minimum B2 Level certification is mandatory in German Language Preferred Qualifications: - Preferred qualifications - Bachelor's degree or above in computer science, computer engineering, or related field, or experience in communicating technically, at a level appropriate for the audience - Proven experience in data annotation and labeling for ML model training and evaluation - Demonstrated ability to adapt to evolving technologies and methodologies in the ML domain Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.", "summaryDescription" : "Catalog Specialist, WWRR Science Annotation Team at Amazon. Skills: Data annotation, ML models. Work closely with product, technology, and science teams. Perform data annotation required to train and evaluate", "postedDate" : "2026-06-13T10:35:05.241", "createdAt" : "2026-06-13T10:35:05.241", "lastUpdated" : "2026-06-15T04:43:01.861", "ingestTimestamp" : "2026-06-13T10:35:05.241", "mongoId" : "69e1b5d82beec4dd3d1ac3fb", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "sourceUrl" : "https://account.amazon.com/jobs/10382953/apply", "salaryBand" : "200k+", "hasSalary" : false, "allSkills" : [ "Analytical skills", "Problem-solving skills", "Time management skills", "Data annotation", "ML models", "Annotation processes", "Manage multiple simultaneous projects", "Reverse logistics processes", "ML domain" ], "skillCount" : 9, "jobCategory" : "operations", "dataQualityScore" : 0.8888889, "_score" : 834.27606, "daysOld" : 1, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "E-commerce" }, { "id" : "68c2e420f8b1b0376634c4b3", "title" : "Catalog Specialist, WWRR Science Annotation Team", "company" : "ADCI HYD", "industry" : "Fulfillment Operations Management, Catalog, Retail", "salary" : { "min" : 750000.0, "max" : 1250000.0, "currency" : "INR", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for a Mid-seniority Catalog Specialist in Hyderabad, India, adjusted for the E-Commerce industry and company size." }, "location" : "Hyderabad, Telangana, IND", "locations" : [ "Hyderabad, Telangana, IND" ], "timestamp" : "2026-06-15T01:29:27.671", "fingerprint" : "01df7b504cb641afc4c01db94a9992e122c357cb8f887dea72a5f9302560dac2", "source" : "amazon", "language" : "en", "applyUrl" : "https://account.amazon.com/jobs/3078239/apply", "externalId" : "2c135fb0-8490-41b4-a4d3-63c6a1ad20b8", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Hyderabad", "state" : "Telangana", "country" : "India", "continent" : "as", "source" : "llm", "workMode" : "onsite", "geo" : "17.3605890,78.4740613" } ], "locationText" : "Hyderabad, Telangana, India", "employmentType" : "FULL_TIME", "minimumEducation" : "Bachelor's", "experienceMinYears" : 1, "experienceMaxYears" : 5, "experienceLevel" : "Mid", "technicalSkills" : "Machine Learning, ML models, Classification algorithms", "professionalSkills" : [ "Data annotation", "ML model training", "ML model evaluation" ], "hardSkillsList" : [ "Data annotation", "ML model training", "ML model evaluation", "Classification algorithms" ], "softSkillsList" : [ "Leadership", "Analytical skills", "Problem-solving skills" ], "hardSkills" : [ { "name" : "Data annotation", "level" : "intermediate" }, { "name" : "ML model training", "level" : "intermediate" }, { "name" : "ML model evaluation", "level" : "intermediate" }, { "name" : "Classification algorithms", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Leadership" }, { "name" : "Analytical skills" }, { "name" : "Problem-solving skills" } ], "majorSkills" : "Data annotation, ML models, Classification algorithms", "majorSkillsList" : [ "Data annotation", "ML models", "Classification algorithms" ], "coreResponsibilities" : "Work closely with teams to support ML models, Perform data annotation to train and evaluate ML, Support data scientists in development of classification algorithms, Collaborate with cross-functional teams to ensure data annotation, Maintain high-quality standards for annotated data, Continuously evaluate and improve annotation processes, Deep-dive on complex problems, Manage multiple simultaneous projects", "teamCollaboration" : "Cross-functional teams; Product teams; Technology teams; Science teams", "projectManagementSkills" : [ "Time management" ], "domainKnowledge" : "Machine Learning; Classification algorithms; Reverse logistics", "domainList" : [ "machine-learning", "classification-algorithms", "reverse-logistics" ], "domainSpecificKnowledge" : [ "Machine Learning", "Classification algorithms", "Reverse logistics" ], "toolProficiency" : [ "Microsoft Office" ], "communicationSkills" : "Frequent communication", "innovationCreativity" : "Innovate", "problemSolvingSkills" : "Problem-solving skills", "outcomesObjectives" : "Delight customers through new experiences", "qualificationsBasic" : "Bachelor's degree, Speak, write, and read fluently in Italian, Experience with Microsoft Office products and applications, 1+ years of proven experience in data annotation and labeling for ML model training and evaluation, Minimum B2 Language Proficiency required", "qualificationsPreferred" : "Experience working on the MTurk platform for data annotation tasks, Proven experience in data annotation and labeling for ML model training and evaluation, Understanding of data annotation methodologies and tools, Familiarity with Amazon's product and category ecosystem, Previous exposure to machine learning concepts and algorithms, Demonstrated ability to adapt to evolving technologies and methodologies in the ML domain", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "In the Worldwide Returns, ReCommerce & Sustainability (WW RR&S) group at Amazon, we are dedicated to ‘making zero happen’ – zero cost of returns, zero waste, and zero defects – to benefit our customers, company, and environment. We are an agile and inclusive organization that constantly innovates to create long-term value by investing in our people and our planet, not simply focusing on the bottom line. WW R&R includes business, product, operations, data, and software engineering teams, who together manage the lifecycle of returned and damaged products. In WW R&R, you will partner across these teams to help customers discover great deals on quality used, rentals, and open box items; get the most value out of Amazon’s products; improve the customer returns experience; and reduce defects, waste, and cost in reverse logistics processes. You will be a leader, a builder, and an owner, collaborating cross-functionally with technical, operations, and business teams to design scalable and automated solutions to customer problems. Amazon is Earth’s most customer-centric company and in WW R&R, the Earth is our customer too. Come join us and innovate with the Amazon Worldwide Returns, ReCommerce & Sustainability team! We are hiring an experienced Catalog Specialist to help us grow our business in innovative ways. In this role, you will work closely with our product, technology and science teams to support new Machine Learning (ML) models and data science classification algorithm development – all helping to delight our customers through new experiences throughout their Amazon shopping journey. Need candidates in language proficiency in: German Key job responsibilities • Work closely with our product, technology, and science teams to support Machine Learning (ML) models • Perform data annotation required to train and evaluate ML models effectively • Support data scientists in the development of classification algorithms • Collaborate with cross-functional teams to ensure data annotation tasks align with project objectives and timelines • Maintain high-quality standards for annotated data to optimize model performance • Continuously evaluate and improve annotation processes to enhance efficiency and accuracy • Strong analytical skills and the ability to deep-dive on complex problems • Ability to manage multiple simultaneous projects requiring frequent communication, organization/time management and problem-solving skills Basic Qualifications: Bachelor's degree Speak, write, and read fluently in Italian Experience with Microsoft Office products and applications 1+ years of proven experience in data annotation and labeling for ML model training and evaluation Minimum B2 Language Proficiency required Preferred Qualifications: Experience working on the MTurk platform for data annotation tasks Proven experience in data annotation and labeling for ML model training and evaluation Understanding of data annotation methodologies and tools Familiarity with Amazon's product and category ecosystem Previous exposure to machine learning concepts and algorithms Demonstrated ability to adapt to evolving technologies and methodologies in the ML domain Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.", "summaryDescription" : "Catalog Specialist, WWRR Science Annotation Team at ADCI HYD. Skills: Data annotation, ML models, Classification algorithms. Work closely with teams to support ML models. Perform data annotation to train and evaluate ML", "postedDate" : "2026-06-13T06:34:52.207", "createdAt" : "2026-06-13T06:34:52.207", "lastUpdated" : "2026-06-15T01:29:27.671", "ingestTimestamp" : "2026-06-13T06:34:52.207", "mongoId" : "68c2e420f8b1b0376634c4b3", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "sourceUrl" : "https://account.amazon.com/jobs/3078239/apply", "salaryBand" : "200k+", "hasSalary" : false, "allSkills" : [ "Data annotation", "ML model training", "ML model evaluation", "Classification algorithms", "Leadership", "Analytical skills", "Problem-solving skills", "ML models", "Time management", "Machine Learning", "Reverse logistics", "Microsoft Office" ], "skillCount" : 12, "jobCategory" : "operations", "dataQualityScore" : 1.0, "_score" : 830.20605, "daysOld" : 1, "hasLocation" : true, "hasGeo" : true }, { "id" : "693072a570f0b99d58ffb6eb", "title" : "Amazon Robotics Annotation Analyst", "company" : "Amazon Support Services Costa Rica SRL", "industry" : "Administrative Support, Planning/Development, Logistics", "salary" : { "min" : 1.4E7, "max" : 2.0E7, "currency" : "CRC", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "The estimation is based on the market baseline for a Mid-level Technology role in San Jose, Costa Rica, adjusted for company size and industry." }, "location" : "CRI", "locations" : [ "CRI" ], "timestamp" : "2026-06-15T01:32:11.737", "fingerprint" : "9301f4574a10785bc0f8d660dcfee5d62a2423540a04b45784d547e4bda59058", "source" : "amazon", "language" : "en", "applyUrl" : "https://account.amazon.com/jobs/3134225/apply", "externalId" : "cca5818e-bc4a-4d1d-aa6f-848218cd911e", "industryFunction" : { }, "structuredLocations" : [ { "country" : "Costa Rica", "continent" : "na", "workMode" : "onsite" } ], "locationText" : "Costa Rica", "employmentType" : "FULL_TIME", "technicalSkills" : "SQL, Excel, Shell scripts", "professionalSkills" : [ "Data annotation", "AI/machine learning support", "Robotics operations", "Annotation standards", "Quality standards", "KPI definition" ], "hardSkillsList" : [ "Object segmentation", "Classification", "Written description", "Detection", "Troubleshooting techniques", "Validation scripts", "Process gap identification" ], "softSkillsList" : [ "Adaptability", "Curiosity" ], "hardSkills" : [ { "name" : "Object segmentation", "level" : "intermediate" }, { "name" : "Classification", "level" : "intermediate" }, { "name" : "Written description", "level" : "intermediate" }, { "name" : "Detection", "level" : "intermediate" }, { "name" : "Troubleshooting techniques", "level" : "intermediate" }, { "name" : "Validation scripts", "level" : "intermediate" }, { "name" : "Process gap identification", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Adaptability" }, { "name" : "Curiosity" } ], "majorSkills" : "Data annotation, Robotics, AI model training", "majorSkillsList" : [ "Data annotation", "Robotics", "AI model training" ], "coreResponsibilities" : "Perform multi-modal data annotations, Collaborate with scientists and engineers to translate ambiguous, Conduct remote tele-planning and tele-operation activities to support, Define quality standards and key performance indicators (KPIs), Validate annotated data using troubleshooting techniques and validation, Analyze the relationship between ground truth data quality, Identify process gaps and improvement opportunities, Implement proactive measures to maintain high-quality output, Communicate project status, challenges, and recommendations to technical", "teamCollaboration" : "Collaborate with scientists and engineers; Communicate project status to technical teams and stakeholders", "domainKnowledge" : "Robotics; Artificial intelligence; Human-AI collaboration; Autonomous systems; Manipulation robotics; Mobile robotic systems", "domainList" : [ "robotics", "artificial-intelligence", "human-ai-collaboration", "autonomous-systems", "manipulation-robotics", "mobile-robotic-systems" ], "domainSpecificKnowledge" : [ "Robotics", "Artificial intelligence", "Human-AI collaboration", "Autonomous systems", "Manipulation robotics", "Mobile robotic systems" ], "toolProficiency" : [ "CVAT", "Labelbox", "SageMaker Ground Truth" ], "communicationSkills" : "Written reports; Presentations", "documentationSkills" : "Annotation standards; Standard operating procedures (SOPs); Best practices", "innovationCreativity" : "Create annotation standards; Define annotation approaches; Shape next-generation AI robotics", "problemSolvingSkills" : "Troubleshooting techniques; Process gap identification; Structured problem-solving", "specialRequirements" : "Travel to the United States approximately 1-2 times per year for one week per trip", "outcomesObjectives" : "Ensure accuracy and consistency of annotated data; Maintain high-quality output", "qualificationsBasic" : "Bachelor's Degree in Computer Science, Engineering, or related technical field, OR 3+ years of professional experience in data annotation, AI/machine learning support, or robotics operations, 2+ years of experience collaborating with science or engineering teams to define dataset requirements for training AI models and converting those requirements into standard operating procedures, 2+ years of experience performing multi-modal data annotations including object segmentation, classification, written description, and detection, 1+ years of experience defining quality standards and KPIs for data annotation projects, Experience with basic technical tools including Excel, SQL queries, reporting tools, and shell scripts, Experience working with both Windows and Mac operating systems, Demonstrated written and verbal communication skills in English, including the ability to deliver presentations to technical audiences, Experience working in ambiguous environments with minimal predefined structure", "qualificationsPreferred" : "Master's Degree in Computer Science, Engineering, or related technical field, Experience with remote tele-planning or tele-operation of robotic systems, Experience working with industry data labeling tools such as Computer Vision Annotation Tool (CVAT), Labelbox, or SageMaker Ground Truth, Familiarity with manipulation robotics and mobile robotic systems, Experience using structured problem-solving techniques such as five-whys analysis or root cause analysis, Understanding of testing methodologies such as A testing for dataset validation and performance evaluation, Experience configuring and using open-source tools to improve or validate ground truth data quality, Experience managing relationships with senior technical stakeholders across multiple teams, Track record of working independently with minimal direction in fast-paced, rapidly changing environments", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "Are you ready to shape the future of robotics through human-AI collaboration? Join us in a role where your judgment and adaptability directly impact how Amazon's robotic systems learn and evolve. You'll work at the intersection of artificial intelligence and robotics, helping define how machines understand the world through your expert data annotations, remote tele-planning, and real-time operational support. This isn't traditional data labeling. You will collaborate with scientists and engineers on ambiguous, cutting-edge projects where standard procedures don't yet exist, and your insights will help create them. Key job responsibilities • Perform multi-modal data annotations including object segmentation, classification, written descriptions, and detection for robotics and AI model training • Collaborate with scientists and engineers to translate ambiguous technical requirements into clear annotation standards and standard operating procedures (SOPs) • Conduct remote tele-planning and tele-operation activities to support robotics systems development and testing • Define quality standards and key performance indicators (KPIs) for annotation projects where established procedures do not yet exist • Validate annotated data using troubleshooting techniques and validation scripts to ensure accuracy and consistency • Analyze the relationship between ground truth data quality and AI model performance metrics such as accuracy, precision, and false positive rates • Identify process gaps and improvement opportunities, then implement proactive measures to maintain high-quality output • Communicate project status, challenges, and recommendations to technical teams and stakeholders through written reports and presentations • Travel to the United States approximately 1-2 times per year for one week per trip A day in the life Your day starts reviewing new robotics tasks requiring annotation standards built from scratch. You'll collaborate with research scientists via video calls to understand model requirements, then design annotation approaches and quality metrics. Mid-morning, you might annotate complex robotic manipulation scenarios across video, image, or text data—providing the foundation that trains autonomous systems. Afternoons involve analyzing quality trends, identifying improvements, and documenting best practices for future scale. You'll alternate between focused annotation work on multi-modal data and collaborative problem-solving sessions where your expertise shapes next-generation AI robotics. The variety keeps every day engaging, with direct impact on robotic systems deployed across Amazon's network. About the team Amazon Robotics develops state-of-the-art robotics for Amazon's Fulfillment Network, which handles more individual items than any company in the world. We develop autonomously driving mobile robots and complex manipulation robots to solve real-world problems at scale. Within Amazon Robotics, the Global Operations - Artificial Intelligence (GO-AI) team enables Amazon to accelerate and scale our robotic and automation solutions through human supervisory control. We partner across Amazon's World Wide Operations organization to deliver data annotation services, ground truth data, and operational support that powers AI innovation. Our team thrives on solving ambiguous challenges, building processes where none exist, and maintaining the highest quality standards. We value curiosity, collaboration, and the ability to translate complex technical requirements into actionable workflows. As we expand our capabilities in remote operations and AI training, you'll help define what success looks like for this emerging field. Basic Qualifications: - Bachelor's Degree in Computer Science, Engineering, or related technical field, OR 3+ years of professional experience in data annotation, AI/machine learning support, or robotics operations - 2+ years of experience collaborating with science or engineering teams to define dataset requirements for training AI models and converting those requirements into standard operating procedures - 2+ years of experience performing multi-modal data annotations including object segmentation, classification, written description, and detection - 1+ years of experience defining quality standards and KPIs for data annotation projects - Experience with basic technical tools including Excel, SQL queries, reporting tools, and shell scripts - Experience working with both Windows and Mac operating systems - Demonstrated written and verbal communication skills in English, including the ability to deliver presentations to technical audiences - Experience working in ambiguous environments with minimal predefined structure Preferred Qualifications: - • Master's Degree in Computer Science, Engineering, or related technical field - • Experience with remote tele-planning or tele-operation of robotic systems - • Experience working with industry data labeling tools such as Computer Vision Annotation Tool (CVAT), Labelbox, or SageMaker Ground Truth - • Familiarity with manipulation robotics and mobile robotic systems - • Experience using structured problem-solving techniques such as five-whys analysis or root cause analysis - • Understanding of testing methodologies such as A/B testing for dataset validation and performance evaluation - • Experience configuring and using open-source tools to improve or validate ground truth data quality - • Experience managing relationships with senior technical stakeholders across multiple teams - • Track record of working independently with minimal direction in fast-paced, rapidly changing environments Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.", "summaryDescription" : "Amazon Robotics Annotation Analyst at Amazon Support Services Costa Rica SRL. Skills: Data annotation, Robotics, AI model training. Perform multi-modal data annotations. Collaborate with scientists and engineers to translate ambiguous", "postedDate" : "2026-06-13T01:15:29.835", "createdAt" : "2026-06-13T01:15:29.835", "lastUpdated" : "2026-06-15T01:32:11.737", "ingestTimestamp" : "2026-06-13T01:15:29.835", "mongoId" : "693072a570f0b99d58ffb6eb", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "sourceUrl" : "https://account.amazon.com/jobs/3134225/apply", "salaryBand" : "200k+", "hasSalary" : false, "allSkills" : [ "Object segmentation", "Classification", "Written description", "Detection", "Troubleshooting techniques", "Validation scripts", "Process gap identification", "Adaptability", "Curiosity", "Data annotation", "Robotics", "AI model training", "AI/machine learning support", "Robotics operations", "Annotation standards", "Quality standards", "KPI definition", "Artificial intelligence", "Human-AI collaboration", "Autonomous systems", "Manipulation robotics", "Mobile robotic systems", "CVAT", "Labelbox", "SageMaker Ground Truth" ], "skillCount" : 25, "jobCategory" : "operations", "dataQualityScore" : 0.8888889, "_score" : 779.25604, "daysOld" : 2, "hasLocation" : true, "hasGeo" : false }, { "id" : "6a2ca19574d8ee00a09a5ee0", "title" : "Data Annotation Team Leader", "industry" : "Data", "salary" : { "min" : 185000.0, "max" : 275000.0, "currency" : "AED", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market data for a Lead Data Annotation Team Leader in Dubai, United Arab Emirates, adjusted for the technology industry and full-time employment." }, "location" : "Dubai", "locations" : [ "Dubai" ], "timestamp" : "2026-06-16T00:45:16.946", "fingerprint" : "f4b3cbedc388016cbae863cb68f5be2f55cf21cb16d7534ec14bd5267ef3299c", "source" : "lever", "language" : "en", "applyUrl" : "https://jobs.lever.co/weride/9d4fea10-e056-4ebe-8459-45cf00a9fcd0/apply", "externalId" : "9d4fea10-e056-4ebe-8459-45cf00a9fcd0", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Dubai", "country" : "United Arab Emirates", "continent" : "as", "source" : "llm", "workMode" : "onsite", "geo" : "25.0742823,55.1885387" } ], "locationText" : "Dubai, United Arab Emirates", "employmentType" : "FULL_TIME", "minimumEducation" : "Bachelor's", "experienceMinYears" : 2, "experienceMaxYears" : 15, "experienceLevel" : "Lead", "technicalSkills" : "Data analysis", "professionalSkills" : [ "Root cause analysis", "Process enhancement", "Efficiency strategies" ], "hardSkillsList" : [ "Descriptive statistics", "Data cleaning", "Data preprocessing" ], "hardSkills" : [ { "name" : "Descriptive statistics", "level" : "intermediate" }, { "name" : "Data cleaning", "level" : "intermediate" }, { "name" : "Data preprocessing", "level" : "intermediate" } ], "majorSkills" : "Data annotation, Team leadership, Quality control", "majorSkillsList" : [ "Data annotation", "Team leadership", "Quality control" ], "coreResponsibilities" : "Lead training and development of overseas data annotators, Provide daily guidance and Q&A support, Document recurring challenges, Perform root-cause analysis, Implement effective solutions, Monitor feedback and acceptance outcomes, Refine annotation methodologies, Enhance annotation guidelines, Design quality control framework, Establish evaluation metrics, Conduct comprehensive audits, Prevent substandard data progression, Anticipate operational risks, Identify team management challenges, Recommend process enhancements, Recommend efficiency strategies, Own project timelines, Own quality benchmarks, Plan workloads strategically, Coordinate resources effectively, Deliver high-quality data on schedule", "teamCollaboration" : "Collaborate with stakeholders", "projectManagementSkills" : [ "Workload planning", "Resource coordination" ], "domainKnowledge" : "Data annotation guidelines", "domainList" : [ "data-annotation-guidelines" ], "domainSpecificKnowledge" : [ "Data annotation guidelines" ], "toolProficiency" : [ "Data analysis tools" ], "communicationSkills" : "Technical documentation", "documentationSkills" : "Annotation methodologies; Annotation guidelines", "problemSolvingSkills" : "Root cause analysis", "outcomesObjectives" : "Elevate team performance and consistency; Ensure quality standards are met; Exceed quality standards; Strengthen overall project execution; Surpass client expectations", "qualificationsBasic" : "Bachelor's degree or higher, Minimum 2.5 years experience in data annotation, English and Chinese proficiency (written and spoken), Able to read and interpret English annotation guidelines, Familiar with fundamental data analysis techniques, Proficient in using data analysis tools", "qualificationsPreferred" : "Experience in Data Annotation", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "## Key Responsibilities 1. Lead, Train & Empower Global Teams Drive the training and development of overseas data annotators, serving as the go-to expert for daily guidance and Q&A support. Proactively document recurring challenges, perform root-cause analysis, and implement effective solutions to elevate team performance and consistency. 2. Turn Feedback into Continuous Improvement Collaborate closely with stakeholders to monitor feedback and acceptance outcomes. Translate insights into actionable improvements by refining annotation methodologies and enhancing guidelines — ensuring quality standards are not only met but consistently exceeded. 3. Champion Quality Excellence Design and implement a robust quality control framework that oversees the entire annotation lifecycle. Establish clear evaluation metrics, conduct comprehensive audits, and safeguard project integrity by preventing substandard data from progressing downstream. 4. Optimize Processes & Drive Efficiency Anticipate operational risks and identify potential team management challenges before they arise. Leverage hands-on experience to recommend smart process enhancements and efficiency strategies that strengthen overall project execution. 5. Deliver with Precision & Accountability Own project timelines and quality benchmarks by strategically planning workloads and coordinating resources effectively. Ensure high-quality data is delivered on schedule, meeting — and surpassing — client expectations. ## Job Requirements 1. Education & Experience Bachelor’s degree or higher in a relevant field. Minimum 2.5 years experience in data annotation is required. Able to quickly understand project requirements and adapt to operational workflows. 2. Language Proficiency English and Chinese (written and spoken) is a MUST. Able to read and interpret English annotation guidelines and technical documentation accurately. Capable of supporting data annotation tasks for international projects. 3. Technical & Analytical Skills Familiar with fundamental data analysis techniques, including descriptive statistics, data cleaning, and preprocessing. Proficient in using data analysis tools to perform basic analysis on annotated datasets. Able to provide data-driven insights and recommendations to support annotation quality and decision-making. Experience in Data Annotation is preferable.", "summaryDescription" : "Data Annotation Team Leader. Skills: Data annotation, Team leadership, Quality control. Lead training and development of overseas data annotators. Provide daily guidance and Q&A support", "postedDate" : "2026-06-11T18:17:15.538", "createdAt" : "2026-06-11T18:17:15.538", "lastUpdated" : "2026-06-16T00:45:16.946", "ingestTimestamp" : "2026-06-14T21:45:57.813", "mongoId" : "6a2ca19574d8ee00a09a5ee0", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryBand" : "150k-200k", "hasSalary" : false, "allSkills" : [ "Descriptive statistics", "Data cleaning", "Data preprocessing", "Data annotation", "Team leadership", "Quality control", "Root cause analysis", "Process enhancement", "Efficiency strategies", "Workload planning", "Resource coordination", "Data annotation guidelines", "Data analysis tools" ], "skillCount" : 13, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Lead", "dataQualityScore" : 0.8888889, "_score" : 762.758, "daysOld" : 4, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.95 }, { "id" : "6a2ca19574d8ee00a09a5ee2", "title" : "Data Annotator", "industry" : "Data", "salary" : { "min" : 60000.0, "max" : 90000.0, "currency" : "AED", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Entry-level Data Annotator salaries in Dubai, UAE, are estimated based on 2024-2025 market data for the technology industry, adjusted for entry seniority and city tier." }, "location" : "Dubai", "locations" : [ "Dubai" ], "timestamp" : "2026-06-16T04:47:59.322", "fingerprint" : "8f46bb98a343194a6e2e9d0efccc1ee30537b615e65b2c3c09169fde86b9219e", "source" : "lever", "language" : "en", "applyUrl" : "https://jobs.lever.co/weride/2046f079-88a3-47fd-8bb1-660e7c45652b/apply", "externalId" : "2046f079-88a3-47fd-8bb1-660e7c45652b", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Dubai", "country" : "United Arab Emirates", "continent" : "as", "source" : "llm", "workMode" : "onsite", "geo" : "25.0742823,55.1885387" } ], "locationText" : "Dubai, United Arab Emirates", "employmentType" : "FULL_TIME", "coreResponsibilities" : "Label and categorize large datasets, Review and correct data annotations, Understand project-specific guidelines, Provide feedback to improve annotation process, Meet daily/weekly annotation targets", "teamCollaboration" : "Work closely with AI teams; Work closely with Machine Learning teams", "outcomesObjectives" : "Maintain high-quality standards", "qualificationsBasic" : "Fluent in English (B2 or above), Basic computer skills, Familiarity with online tools, Ability to follow detailed instructions, Adapt to changing project needs, Experience in Data Annotation is a MUST", "qualificationsPreferred" : "Prior experience in data annotation or related fields is a plus", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "## Key Responsibilities Accurately label and categorize large datasets, including text, audio, images, and video. Review and correct data annotations to ensure quality and consistency. Work closely with the AI and Machine Learning teams to understand project-specific guidelines. Provide feedback to improve the data annotation process. Meet daily/weekly annotation targets while maintaining high-quality standards. ## Requirements Fluent in English (B2 or above). Basic computer skills and familiarity with online tools. Ability to follow detailed instructions and adapt to changing project needs. Prior experience in data annotation or related fields is a plus but not required. Experience in Data Annotation is a MUST.", "summaryDescription" : "Data Annotator. Label and categorize large datasets. Review and correct data annotations", "postedDate" : "2026-06-11T18:21:42.539", "createdAt" : "2026-06-11T18:21:42.539", "lastUpdated" : "2026-06-16T04:47:59.322", "ingestTimestamp" : "2026-06-14T21:24:20.314", "mongoId" : "6a2ca19574d8ee00a09a5ee2", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryBand" : "50k-75k", "hasSalary" : false, "allSkills" : [ ], "skillCount" : 0, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.6666667, "_score" : 724.6112, "daysOld" : 4, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.95 }, { "id" : "69c94a759bc74574cbbb3da1", "title" : "AI Data Annotation Specialist (French)", "company" : "Amazon MLS Poland Sp. z o.o. - K69", "industry" : "Administrative Support, Machine Learning Data Operatio, Artificial Intelligence", "salary" : { "min" : 75000.0, "max" : 110000.0, "currency" : "PLN", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for an AI Data Annotation Specialist in Gdansk, Poland, adjusted for mid-seniority and the technology industry." }, "location" : "Gdansk, Pomeranian Voivodeship, POL", "locations" : [ "Gdansk, Pomeranian Voivodeship, POL" ], "timestamp" : "2026-06-14T23:54:59.758", "fingerprint" : "02815d1d8cff5fff3e421ad50d6097cdba7686e6a403074f70b698cd6e0deeb7", "source" : "amazon", "language" : "en", "applyUrl" : "https://account.amazon.com/jobs/3191110/apply", "externalId" : "85ff774f-1a80-4c65-96a8-5aef6c520643", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Gdansk", "state" : "Pomeranian Voivodeship", "country" : "Poland", "continent" : "eu", "source" : "llm", "workMode" : "onsite", "geo" : "54.4288032,18.7983270" } ], "locationText" : "Gdansk, Pomeranian Voivodeship, Poland", "employmentType" : "FULL_TIME", "professionalSkills" : [ "Data labelling", "Dialogue evaluation" ], "hardSkillsList" : [ "Root cause analysis", "Error pattern identification" ], "softSkillsList" : [ "Eye for detail", "Individuality", "Creative mind" ], "hardSkills" : [ { "name" : "Root cause analysis", "level" : "intermediate" }, { "name" : "Error pattern identification", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Eye for detail" }, { "name" : "Individuality" }, { "name" : "Creative mind" } ], "majorSkills" : "Data annotation, LLM development", "majorSkillsList" : [ "Data annotation", "LLM development" ], "coreResponsibilities" : "Maintain strict confidentiality, Work with different data types, Deliver high-quality labelled data, Meet KPIs, Generate high quality human insight data, Make sound judgments, Make logical decisions, Demonstrate support on daily operational deliverables, Analyze root causes, Identify error patterns, Propose solutions, Enhance quality of labeling tasks, Identify process issues, Identify operational issues, Suggest changes to unblock operations, Provide floor support, Clarify internal queries", "teamCollaboration" : "Diverse team; Daily operational deliverables", "domainKnowledge" : "Generative AI; Large language models", "domainList" : [ "generative-ai", "large-language-models" ], "domainSpecificKnowledge" : [ "Generative AI", "Large language models" ], "toolProficiency" : [ "In-house tools" ], "communicationSkills" : "Business writing skills; Professional correspondence", "documentationSkills" : "Create reports; Create proposals", "continuousLearning" : "Trainings", "innovationCreativity" : "Invent; Simplify; Build", "problemSolvingSkills" : "Structured problem-solving; Root cause analysis; Error pattern identification", "outcomesObjectives" : "Meet our KPIs", "qualificationsBasic" : "Associate's Degree or related work experience, C1+ or equivalent fluency in French, B2+ in spoken and written English, Business writing skills, Advanced reading comprehension, Developed analytical thinking, Structured problem-solving capabilities, Interpret and implement detailed instructions, Proficient research skills, Gathering and synthesizing information, Proven attention to detail", "qualificationsPreferred" : "Bachelor's degree in a relevant field, 2+ years of professional work experience, Demonstrated task execution ability, Leverage open-source resources effectively, Adapt to fast-paced environments, 2-3 years project coordination experience, 2-3 years project management experience, Experience managing stakeholder relationships, Advanced proficiency in Microsoft Office Suite, Advanced proficiency in common business applications", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "AI is the most transformational technology of our time, capable of tackling some of humanity’s most challenging problems. Amazon is investing in generative AI and the responsible development and deployment of large language models (LLMs) across all of our businesses. Come build the future of human-technology interaction with us. We are looking for those candidates who just don’t think out of the box, but make the box they are in ‘Bigger’. The future is now, do you want to be a part of it? Then read on! Key job responsibilities - Maintain and follow strict confidentiality as customer privacy is our most important tenet - Work with a range of different types of data including, but not limited to: text, speech, audio, image, and video - Deliver high-quality labelled data, using guidelines provided to meet our KPIs and using in-house tools and software, as part of Amazon's commitment to developing and deploying AI responsibly. - Demonstrate proficiency in generating high quality human insight data across a range of modalities, inclusive of text, image video and audio. - Capable of making sound judgments and logical decisions when faced with ambiguous or incomplete information while performing tasks. - Eye for detail and ability to pivot from one category of requirement to another instantaneously. - Demonstrate support on daily operational deliverables for multiple task types assigned to you and the team - Analyze root causes, identify error patterns, and propose solutions to enhance the quality of labeling tasks and their outputs. - Responsible for identifying day-to-day process and operational issues in Standard Operating Procedure, tools and suggest changes to unblock operations - Demonstrate ownership in floor support to clarify internal queries during execution on need basis A day in the life We are looking for a ML Data Associate (MLDA) to undertake the task of foundational labeling functions, such as dialogue evaluation on speech, text, audio, video data. Your ability to concentrate, multi-task and your high attention to detail helps you deliver high-quality work as well as maintaining strict confidentiality and follow all applicable Amazon policies for securing confidential information. You will be a part of a diverse team with the shared vision of improving customers’ lives with practical, useful generative AI innovations. An inner drive, individuality, and a creative mind are extremely beneficial. About the team The team works strictly in the office Monday through Friday. We are constantly looking for ways to improve our capabilities and deliver the best product possible. Diverse team, regular meetings, trainings, and Amazon events throughout the year await you. The minimum base pay salary for this position is listed below. As a total compensation company, Amazon's package may include further pay components such as performance bonuses, where applicable. Your recruiting team will share final base pay and any applicable total compensation elements depending on skills and requirements.Company discretionary, supplementary benefits apply subject to further terms, including pension plans, health insurance, life and disability insurance, business travel insurance, MultiSport card, employee discount code and program (EDC/Extras), commuter benefits where applicable, and the Employee Assistance Program (private and confidential counselling and referral service to support health and well-being).The minimum base pay for this position is PLN 7.916.66 monthly. Wynagrodzenie podstawowe dla tego stanowiska jest wskazane poniżej. Amazon kieruje się zasadą całkowitego wynagrodzenia, w związku z czym oprócz wynagrodzenia zasadniczego mogą być przewidziane dodatkowe składniki wynagrodzenia, takie jak premie (w odpowiednich przypadkach). Zespół rekrutacyjny poinformuje Cię o ostatecznej kwocie wynagrodzenia zasadniczego oraz o wszystkich przysługujących dodatkowych składnikach wynagrodzenia, w zależności od umiejętności i wymagań.Dodatkowe świadczenia przysługują na warunkach obowiązujących w Spółce i obejmują m.in. plany emerytalne, dodatkowe ubezpieczenie zdrowotne, dodatkowe ubezpieczenie na życie, ubezpieczenie podróży służbowych, kartę MultiSport, kod rabatowy dla pracowników i program zniżek pracowniczych (EDC/Extras), dotacje dla osób dojeżdżających (w odpowiednich przypadkach) oraz Program Wsparcia Pracownika (EAP – prywatne i poufne doradztwo i wsparcie).Wynagrodzenie podstawowe dla tego stanowiska wynosi 7,916.66 PLN miesięcznie. Basic Qualifications: - An Associate’s Degree or related work experience - - C1+ or equivalent fluency in French language - - Good working knowledge (B2+) in both spoken and written English - - Strong business writing skills with ability to create reports, proposals, and professional correspondence - - Advanced reading comprehension with ability to analyze complex business documents - - Developed analytical thinking and structured problem-solving capabilities - - Strong ability to interpret and implement detailed instructions across various projects - - Proficient research skills with experience gathering and synthesizing information from multiple sources - - Proven attention to detail in managing complex tasks and documents Preferred Qualifications: - Bachelor’s degree in a relevant field - 2+ years of professional work experience with demonstrated task execution ability - Proven capacity to leverage open-source resources effectively for comprehensive research purposes - Ability to adapt well to fast-paced environments with changing circumstances, direction, and strategy - 2-3 years project coordination or management experience (for support functions teams) - Experience managing stakeholder relationships across departments - Advanced proficiency in Microsoft Office Suite and common business applications. - - Bachelor’s degree in a relevant field - - 2+ years of professional work experience with demonstrated task execution ability - - Proven capacity to leverage open-source resources effectively for comprehensive research purposes - - Ability to adapt well to fast-paced environments with changing circumstances, direction, and strategy - - 2-3 years project coordination or management experience (for support functions teams) - - Experience managing stakeholder relationships across departments - - Advanced proficiency in Microsoft Office Suite and common business applications. Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice ( https://www.amazon.jobs/en/privacy_page ) to know more about how we collect, use and transfer the personal data of our candidates. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.", "summaryDescription" : "AI Data Annotation Specialist (French) at Amazon MLS Poland Sp. z o.o. - K69. Skills: Data annotation, LLM development. Maintain strict confidentiality. Work with different data types", "postedDate" : "2026-06-13T04:28:02.969", "createdAt" : "2026-06-13T04:28:02.969", "lastUpdated" : "2026-06-14T23:54:59.758", "ingestTimestamp" : "2026-06-13T04:28:02.969", "mongoId" : "69c94a759bc74574cbbb3da1", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "sourceUrl" : "https://account.amazon.com/jobs/3191110/apply", "salaryBand" : "75k-100k", "hasSalary" : false, "allSkills" : [ "Root cause analysis", "Error pattern identification", "Eye for detail", "Individuality", "Creative mind", "Data annotation", "LLM development", "Data labelling", "Dialogue evaluation", "Generative AI", "Large language models", "In-house tools" ], "skillCount" : 12, "jobCategory" : "operations", "dataQualityScore" : 0.8888889, "_score" : 709.6979, "daysOld" : 1, "hasLocation" : true, "hasGeo" : true }, { "id" : "6a306073f292fd229cf8dc78", "title" : "Pavement Condition Index (PCI) Survey & Annotation Specialist – Freelance AI Trainer Project", "company" : "Meridial", "salary" : { "min" : 6.0, "max" : 20.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "Worldwide - Remote", "locations" : [ "Worldwide - Remote" ], "timestamp" : "2026-06-16T07:22:37.846", "fingerprint" : "cee893319153a3a26f45d7279ddd48fb28d5a4d98d25442ad9c902aafbdd2fbc", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.eu.greenhouse.io/agency/jobs/4781824101", "externalId" : "4781824101", "industryFunction" : { }, "structuredLocations" : [ { "workMode" : "remote" } ], "locationText" : "", "employmentType" : "CONTRACT", "hardSkillsList" : [ "Pavement condition assessment", "Pavement distress identification" ], "hardSkills" : [ { "name" : "Pavement condition assessment", "level" : "intermediate" }, { "name" : "Pavement distress identification", "level" : "intermediate" } ], "majorSkills" : "PCI surveys, Pavement condition, Annotation tasks", "majorSkillsList" : [ "PCI surveys", "Pavement condition", "Annotation tasks" ], "coreResponsibilities" : "Review roadway images, Review inspection records, Review AI-generated outputs, Determine accuracy of distress types, Determine accuracy of severity levels, Determine accuracy of condition assessments, Annotate pavement features, Validate pavement features, Annotate distress patterns, Validate distress patterns, Analyze roadway imagery, Analyze survey excerpts, Validate AI-generated classifications, Provide written feedback, Clarify inspector distinctions, Clarify sampling unit evaluation, Clarify field condition influence", "domainKnowledge" : "Pavement Condition Index (PCI); Pavement distress types; Pavement severity levels; Pavement condition ratings; Roadway conditions; Infrastructure maintenance planning; Cracking; Rutting; Spalling; Patching; Weathering", "domainList" : [ "pavement-condition-index-pci", "pavement-distress-types", "pavement-severity-levels", "pavement-condition-ratings", "roadway-conditions", "infrastructure-maintenance-planning", "cracking", "rutting", "spalling", "patching", "weathering" ], "domainSpecificKnowledge" : [ "Pavement Condition Index (PCI)", "Pavement distress types", "Pavement severity levels", "Pavement condition ratings", "Roadway conditions", "Infrastructure maintenance planning", "Cracking", "Rutting", "Spalling", "Patching", "Weathering" ], "communicationSkills" : "Written feedback; Explain inspection reasoning; Explain classification decisions", "qualificationsBasic" : "Familiarity with PCI surveys, Attention to visual detail, Ability to apply distress definitions, Ability to apply rating criteria", "qualificationsPreferred" : "Experience with inspection imagery, Experience with condition reports, Experience with roadway maintenance planning", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Are you a pavement professional or roadway inspector with hands-on experience conducting or interpreting Pavement Condition Index (PCI) surveys? Advanced AI systems are being developed to understand roadway conditions, interpret inspection imagery, and support infrastructure maintenance planning. High-quality training data based on real PCI survey practices is essential to ensure these systems accurately recognize pavement distress and condition ratings. This project relies on your expertise to help train and evaluate next-generation AI models. In this project, you will focus specifically on PCI-related workflows and annotation tasks. You will review roadway images, inspection records, and AI-generated outputs to determine whether pavement distress types, severity levels, and condition assessments are accurate and consistent with PCI methodology. A central part of the work involves annotating or validating pavement features and distress patterns so models can better learn to identify cracking, rutting, spalling, patching, weathering, and other common pavement conditions. On a typical day, you may analyze roadway imagery or survey excerpts, validate AI-generated classifications of pavement distress, and provide written feedback explaining where models misidentify features or misinterpret severity levels. You may also help clarify how inspectors distinguish between similar distress categories, how sampling units are evaluated, and how real-world field conditions influence PCI scoring and reporting. This project is well suited for pavement engineers, civil or transportation engineers, DOT specialists, highway inspectors, and other professionals who have conducted or reviewed PCI surveys or pavement condition assessments. Experience working with inspection imagery, condition reports, or roadway maintenance planning is highly valuable. Clear written communication and the ability to explain inspection reasoning and classification decisions in a structured way are essential. No prior AI or technical background is required. Training and project guidelines will be provided. What matters most is your familiarity with PCI surveys, attention to visual detail, and ability to consistently apply pavement distress definitions and rating criteria. We offer a pay range of $6 - $20 per hour, with the exact rate determined after evaluating your experience, expertise, and geographic location. Final offer amounts may vary from the pay range listed above. As a contractor, you will supply a secure computer and high-speed internet; company-sponsored benefits such as health insurance and PTO do not apply. Job title: Pavement Condition Index (PCI) Survey & Annotation Specialist – Freelance AI Trainer Project Employment type: Freelance / Contract Workplace type: Remote", "summaryDescription" : "Pavement Condition Index (PCI) Survey & Annotation Specialist – Freelance AI Trainer Project at Meridial. Skills: PCI surveys, Pavement condition, Annotation tasks. Review roadway images. Review inspection records", "postedDate" : "2026-06-15T23:29:29.926", "createdAt" : "2026-06-15T23:29:29.926", "lastUpdated" : "2026-06-16T07:22:37.846", "ingestTimestamp" : "2026-06-15T23:29:29.926", "mongoId" : "6a306073f292fd229cf8dc78", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 12480, "salaryAnnualMax" : 41600, "salaryBand" : "<30k", "hasSalary" : true, "allSkills" : [ "Pavement condition assessment", "Pavement distress identification", "PCI surveys", "Pavement condition", "Annotation tasks", "Pavement Condition Index (PCI)", "Pavement distress types", "Pavement severity levels", "Pavement condition ratings", "Roadway conditions", "Infrastructure maintenance planning", "Cracking", "Rutting", "Spalling", "Patching", "Weathering" ], "skillCount" : 16, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 679.66046, "daysOld" : 0, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.92 }, { "id" : "6a306073f292fd229cf8dc7a", "title" : "Pavement Condition Index (PCI) Survey & Annotation Specialist – Freelance AI Trainer Project", "company" : "Meridial", "salary" : { "min" : 6.0, "max" : 20.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "Worldwide - Remote", "locations" : [ "Worldwide - Remote" ], "timestamp" : "2026-06-16T10:21:28.891", "fingerprint" : "e482d56ee0826e185b0d76ea2bb24c65dda4f2144662f7c206ab9e166c67cb13", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.eu.greenhouse.io/agency/jobs/4781822101", "externalId" : "4781822101", "industryFunction" : { }, "structuredLocations" : [ { "workMode" : "remote" } ], "locationText" : "", "employmentType" : "CONTRACT", "hardSkillsList" : [ "Pavement condition assessment", "Pavement distress identification", "Roadway inspection" ], "hardSkills" : [ { "name" : "Pavement condition assessment", "level" : "intermediate" }, { "name" : "Pavement distress identification", "level" : "intermediate" }, { "name" : "Roadway inspection", "level" : "intermediate" } ], "majorSkills" : "PCI surveys, Pavement condition, Annotation tasks", "majorSkillsList" : [ "PCI surveys", "Pavement condition", "Annotation tasks" ], "coreResponsibilities" : "Review roadway images, Review inspection records, Review AI-generated outputs, Determine accuracy of distress types, Determine accuracy of severity levels, Determine accuracy of condition assessments, Annotate pavement features, Validate pavement features, Annotate pavement distress patterns, Validate pavement distress patterns, Analyze roadway imagery, Analyze survey excerpts, Validate AI-generated classifications, Provide written feedback, Clarify inspector distinctions, Clarify sampling unit evaluation, Clarify field condition influence on scoring, Clarify field condition influence on reporting", "domainKnowledge" : "Pavement Condition Index (PCI); Pavement distress types; Pavement severity levels; Pavement condition ratings; Cracking; Rutting; Spalling; Patching; Weathering", "domainList" : [ "pavement-condition-index-pci", "pavement-distress-types", "pavement-severity-levels", "pavement-condition-ratings", "cracking", "rutting", "spalling", "patching", "weathering" ], "domainSpecificKnowledge" : [ "Pavement Condition Index (PCI)", "Pavement distress types", "Pavement severity levels", "Pavement condition ratings", "Cracking", "Rutting", "Spalling", "Patching", "Weathering" ], "communicationSkills" : "Written feedback; Explain reasoning", "qualificationsBasic" : "Familiarity with PCI surveys, Attention to visual detail, Ability to apply distress definitions, Ability to apply rating criteria, Clear written communication", "qualificationsPreferred" : "Experience with inspection imagery, Experience with condition reports, Experience with roadway maintenance planning", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Are you a pavement professional or roadway inspector with hands-on experience conducting or interpreting Pavement Condition Index (PCI) surveys? Advanced AI systems are being developed to understand roadway conditions, interpret inspection imagery, and support infrastructure maintenance planning. High-quality training data based on real PCI survey practices is essential to ensure these systems accurately recognize pavement distress and condition ratings. This project relies on your expertise to help train and evaluate next-generation AI models. In this project, you will focus specifically on PCI-related workflows and annotation tasks. You will review roadway images, inspection records, and AI-generated outputs to determine whether pavement distress types, severity levels, and condition assessments are accurate and consistent with PCI methodology. A central part of the work involves annotating or validating pavement features and distress patterns so models can better learn to identify cracking, rutting, spalling, patching, weathering, and other common pavement conditions. On a typical day, you may analyze roadway imagery or survey excerpts, validate AI-generated classifications of pavement distress, and provide written feedback explaining where models misidentify features or misinterpret severity levels. You may also help clarify how inspectors distinguish between similar distress categories, how sampling units are evaluated, and how real-world field conditions influence PCI scoring and reporting. This project is well suited for pavement engineers, civil or transportation engineers, DOT specialists, highway inspectors, and other professionals who have conducted or reviewed PCI surveys or pavement condition assessments. Experience working with inspection imagery, condition reports, or roadway maintenance planning is highly valuable. Clear written communication and the ability to explain inspection reasoning and classification decisions in a structured way are essential. No prior AI or technical background is required. Training and project guidelines will be provided. What matters most is your familiarity with PCI surveys, attention to visual detail, and ability to consistently apply pavement distress definitions and rating criteria. We offer a pay range of $6 - $20 per hour, with the exact rate determined after evaluating your experience, expertise, and geographic location. Final offer amounts may vary from the pay range listed above. As a contractor, you will supply a secure computer and high-speed internet; company-sponsored benefits such as health insurance and PTO do not apply. Job title: Pavement Condition Index (PCI) Survey & Annotation Specialist – Freelance AI Trainer Project Employment type: Freelance / Contract Workplace type: Remote", "summaryDescription" : "Pavement Condition Index (PCI) Survey & Annotation Specialist – Freelance AI Trainer Project at Meridial. Skills: PCI surveys, Pavement condition, Annotation tasks. Review roadway images. Review inspection records", "postedDate" : "2026-06-15T23:26:26.766", "createdAt" : "2026-06-15T23:26:26.766", "lastUpdated" : "2026-06-16T10:21:28.891", "ingestTimestamp" : "2026-06-15T23:26:26.766", "mongoId" : "6a306073f292fd229cf8dc7a", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 12480, "salaryAnnualMax" : 41600, "salaryBand" : "<30k", "hasSalary" : true, "allSkills" : [ "Pavement condition assessment", "Pavement distress identification", "Roadway inspection", "PCI surveys", "Pavement condition", "Annotation tasks", "Pavement Condition Index (PCI)", "Pavement distress types", "Pavement severity levels", "Pavement condition ratings", "Cracking", "Rutting", "Spalling", "Patching", "Weathering" ], "skillCount" : 15, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 679.5204, "daysOld" : 0, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.92 }, { "id" : "6a306073f292fd229cf8dc7d", "title" : "Pavement Condition Index (PCI) Survey & Annotation Specialist – Freelance AI Trainer Project", "company" : "Meridial", "salary" : { "min" : 6.0, "max" : 20.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "Worldwide - Remote", "locations" : [ "Worldwide - Remote" ], "timestamp" : "2026-06-16T08:06:09.373", "fingerprint" : "98c6b011042b0f27ba3c7a2b62710e2b07e83f1afa741da1c880e3229a2917cd", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.eu.greenhouse.io/agency/jobs/4775898101", "externalId" : "4775898101", "industryFunction" : { }, "structuredLocations" : [ { "workMode" : "remote" } ], "locationText" : "", "employmentType" : "CONTRACT", "hardSkillsList" : [ "Pavement condition assessment", "Pavement distress identification" ], "hardSkills" : [ { "name" : "Pavement condition assessment", "level" : "intermediate" }, { "name" : "Pavement distress identification", "level" : "intermediate" } ], "majorSkills" : "PCI surveys, Pavement condition, Annotation tasks", "majorSkillsList" : [ "PCI surveys", "Pavement condition", "Annotation tasks" ], "coreResponsibilities" : "Review roadway images, Review inspection records, Review AI-generated outputs, Determine accuracy of distress types, Determine accuracy of severity levels, Determine accuracy of condition assessments, Annotate pavement features, Validate pavement features, Annotate distress patterns, Validate distress patterns, Analyze roadway imagery, Analyze survey excerpts, Validate AI-generated classifications, Provide written feedback, Clarify inspector distinctions, Clarify sampling unit evaluation, Clarify field condition influence", "domainKnowledge" : "Pavement Condition Index (PCI); Pavement distress types; Pavement severity levels; Pavement condition ratings; Roadway conditions; Infrastructure maintenance planning; Pavement distress definitions; Rating criteria", "domainList" : [ "pavement-condition-index-pci", "pavement-distress-types", "pavement-severity-levels", "pavement-condition-ratings", "roadway-conditions", "infrastructure-maintenance-planning", "pavement-distress-definitions", "rating-criteria" ], "domainSpecificKnowledge" : [ "Pavement Condition Index (PCI)", "Pavement distress types", "Pavement severity levels", "Pavement condition ratings", "Roadway conditions", "Infrastructure maintenance planning", "Pavement distress definitions", "Rating criteria" ], "communicationSkills" : "Written feedback; Explain inspection reasoning; Explain classification decisions", "qualificationsBasic" : "Familiarity with PCI surveys, Attention to visual detail, Ability to apply distress definitions, Ability to apply rating criteria", "qualificationsPreferred" : "Experience with inspection imagery, Experience with condition reports, Experience with roadway maintenance planning", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Are you a pavement professional or roadway inspector with hands-on experience conducting or interpreting Pavement Condition Index (PCI) surveys? Advanced AI systems are being developed to understand roadway conditions, interpret inspection imagery, and support infrastructure maintenance planning. High-quality training data based on real PCI survey practices is essential to ensure these systems accurately recognize pavement distress and condition ratings. This project relies on your expertise to help train and evaluate next-generation AI models. In this project, you will focus specifically on PCI-related workflows and annotation tasks. You will review roadway images, inspection records, and AI-generated outputs to determine whether pavement distress types, severity levels, and condition assessments are accurate and consistent with PCI methodology. A central part of the work involves annotating or validating pavement features and distress patterns so models can better learn to identify cracking, rutting, spalling, patching, weathering, and other common pavement conditions. On a typical day, you may analyze roadway imagery or survey excerpts, validate AI-generated classifications of pavement distress, and provide written feedback explaining where models misidentify features or misinterpret severity levels. You may also help clarify how inspectors distinguish between similar distress categories, how sampling units are evaluated, and how real-world field conditions influence PCI scoring and reporting. This project is well suited for pavement engineers, civil or transportation engineers, DOT specialists, highway inspectors, and other professionals who have conducted or reviewed PCI surveys or pavement condition assessments. Experience working with inspection imagery, condition reports, or roadway maintenance planning is highly valuable. Clear written communication and the ability to explain inspection reasoning and classification decisions in a structured way are essential. No prior AI or technical background is required. Training and project guidelines will be provided. What matters most is your familiarity with PCI surveys, attention to visual detail, and ability to consistently apply pavement distress definitions and rating criteria. We offer a pay range of $6 - $20 per hour, with the exact rate determined after evaluating your experience, expertise, and geographic location. Final offer amounts may vary from the pay range listed above. As a contractor, you will supply a secure computer and high-speed internet; company-sponsored benefits such as health insurance and PTO do not apply. Job title: Pavement Condition Index (PCI) Survey & Annotation Specialist – Freelance AI Trainer Project Employment type: Freelance / Contract Workplace type: Remote", "summaryDescription" : "Pavement Condition Index (PCI) Survey & Annotation Specialist – Freelance AI Trainer Project at Meridial. Skills: PCI surveys, Pavement condition, Annotation tasks. Review roadway images. Review inspection records", "postedDate" : "2026-06-15T23:26:14.424", "createdAt" : "2026-06-15T23:26:14.424", "lastUpdated" : "2026-06-16T08:06:09.373", "ingestTimestamp" : "2026-06-15T23:26:14.424", "mongoId" : "6a306073f292fd229cf8dc7d", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 12480, "salaryAnnualMax" : 41600, "salaryBand" : "<30k", "hasSalary" : true, "allSkills" : [ "Pavement condition assessment", "Pavement distress identification", "PCI surveys", "Pavement condition", "Annotation tasks", "Pavement Condition Index (PCI)", "Pavement distress types", "Pavement severity levels", "Pavement condition ratings", "Roadway conditions", "Infrastructure maintenance planning", "Pavement distress definitions", "Rating criteria" ], "skillCount" : 13, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 679.511, "daysOld" : 0, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.92 }, { "id" : "6a3078d3f292fd229c02d03a", "title" : "Quality Assurance Auditor", "company" : "PSI CRO", "industry" : "Pharmaceuticals", "salary" : { "min" : 7500000.0, "max" : 1.05E7, "currency" : "JPY", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for a Mid-level Quality Assurance Auditor in Tokyo, Japan, with 3+ years of experience in the Healthcare industry." }, "location" : "Tokyo, Tokyo, JP", "locations" : [ "Tokyo", "Tokyo", "JP" ], "timestamp" : "2026-06-16T10:57:59.794", "fingerprint" : "c6ab4a9384305a665d06d0e4b2af5fa16de4a7d820fa8fca2f2ec84fbaef0a70", "source" : "smartrecruiters", "language" : "en", "applyUrl" : "https://jobs.smartrecruiters.com/PSICRO/744000132166364-quality-assurance-auditor", "externalId" : "744000132166364", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Tokyo", "state" : "Tokyo", "country" : "Japan", "continent" : "as", "source" : "llm", "workMode" : "onsite", "geo" : "35.6812546,139.7667060" } ], "locationText" : "Tokyo, Tokyo, Japan", "employmentType" : "FULL_TIME", "minimumEducation" : "Bachelor's", "experienceMinYears" : 5, "experienceMaxYears" : 10, "experienceLevel" : "mid", "technicalSkills" : "MS Office", "professionalSkills" : [ "Quality management", "Quality assurance", "Study audits", "Vendor qualification", "Internal systems audits", "Process audits" ], "hardSkillsList" : [ "QA study audits", "Internal systems audits", "Internal locations audits", "Vendor qualification audits", "Internal process audits" ], "softSkillsList" : [ "Presentation skills", "Customer-service skills", "Team building skills", "Leadership skills", "Organizational skills" ], "hardSkills" : [ { "name" : "QA study audits", "level" : "intermediate" }, { "name" : "Internal systems audits", "level" : "intermediate" }, { "name" : "Internal locations audits", "level" : "intermediate" }, { "name" : "Vendor qualification audits", "level" : "intermediate" }, { "name" : "Internal process audits", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Presentation skills" }, { "name" : "Customer-service skills" }, { "name" : "Team building skills" }, { "name" : "Leadership skills" }, { "name" : "Organizational skills" } ], "majorSkills" : "Quality assurance, Study audits, Vendor qualification", "majorSkillsList" : [ "Quality assurance", "Study audits", "Vendor qualification" ], "coreResponsibilities" : "Serve as communication point to project teams, Follow up with project teams, Liaise with PSI divisions, Liaise with vendors, Serve as communication point for Operations staff, Serve as communication point for clients, Prepare QA study audits, Conduct QA study audits, Report QA study audits, Assemble QA study audits documentation, Participate in preparation of internal systems audits, Participate in conduct of internal systems audits, Participate in reporting of internal systems audits, Participate in preparation of internal locations audits, Participate in conduct of internal locations audits, Participate in reporting of internal locations audits, Participate in preparation of vendor qualification audits, Participate in conduct of vendor qualification audits, Participate in reporting of vendor qualification audits, Participate in preparation of internal process audits, Participate in conduct of internal process audits, Participate in reporting of internal process audits, Train Operations staff in quality management", "teamCollaboration" : "Communication point to project teams; Liaise with PSI divisions; Liaise with vendors; Communication point for Operations staff; Communication point for clients", "projectManagementSkills" : [ "Quality management" ], "domainKnowledge" : "Life science; Contract Research Organization", "domainList" : [ "life-science", "contract-research-organization" ], "domainSpecificKnowledge" : [ "Life science", "Contract Research Organization" ], "toolProficiency" : [ "MS Project" ], "communicationSkills" : "Presentation skills", "documentationSkills" : "Assemble QA study audits documentation", "qualificationsBasic" : "3 years in quality management, 2 to 3 supervised QA audits, Full working proficiency in English, Full working proficiency in Japanese", "qualificationsPreferred" : "Bachelor or above degree in life science, MS Office applications proficiency, MS Project proficiency", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "PSI is a leading Contract Research Organization with 30 years in the industry offering a perfect balance between stability and innovation to both clients and employees. We focus on delivering quality and on-time services across a variety of therapeutic indications. SCOPE OF RESPONSIBILITIES 1. Communication 1.1. Serves as the communication point to project teams with regard to quality assurance (QA) study audits 1.2. Follows up with project teams with regard to resolution of PSI QA study audits 1.3. Liaises with PSI divisions/operational offices with regard to internal systems, process, and location audits 1.4. Liaises with vendors with regard to vendor qualification audits 1.5. Serves as the communication point for PSI Operations staff with regard to issues related to quality assurance 1.6. Serves as the communication point for PSI clients with regard to QA audits contracted to PSI 2. Planning and scheduling 2.1. Prepares, conducts, and reports QA study audits 2.2. Assembles QA study audits documentation 2.3. Takes part in the preparation, conduct, and reporting of internal systems audits 2.4. Takes part in the preparation, conduct, and reporting of internal locations audits 2.5. Takes part in the preparation, conduct, and reporting of vendor qualification audits 2.6. Takes part in the preparation, conduct, and reporting of internal process audits 3. Training 3.1. Trains PSI Operations staff in quality management topics ## Qualifications 1. Bachelor or above degree in life science or an equivalent combination of education 2. Minimum of 3 years in quality management or quality assurance, 2 to 3 supervised QA audits is a must 3. Full working proficiency in English and Japanese 4. Communication, presentation, and customer-service skills 5. Team building, leadership and organizational skills 6. Proficiency in MS Office applications, including MS Project ## Additional Information Make the right call and take your career to a whole new level. Join the company that focuses on its people and invests in their professional development and success.", "summaryDescription" : "Quality Assurance Auditor at PSI CRO. Skills: Quality assurance, Study audits, Vendor qualification. Serve as communication point to project teams. Follow up with project teams", "postedDate" : "2026-06-15T10:33:18.000", "createdAt" : "2026-06-16T01:38:18.100", "lastUpdated" : "2026-06-16T10:57:59.794", "ingestTimestamp" : "2026-06-16T01:38:18.100", "mongoId" : "6a3078d3f292fd229c02d03a", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryBand" : "200k+", "hasSalary" : false, "allSkills" : [ "QA study audits", "Internal systems audits", "Internal locations audits", "Vendor qualification audits", "Internal process audits", "Presentation skills", "Customer-service skills", "Team building skills", "Leadership skills", "Organizational skills", "Quality assurance", "Study audits", "Vendor qualification", "Quality management", "Process audits", "Life science", "Contract Research Organization", "MS Project" ], "skillCount" : 18, "jobCategory" : "operations", "jobSubcategory" : "general", "seniorityLevel" : "Mid", "dataQualityScore" : 1.0, "_score" : 659.50964, "daysOld" : 1, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Healthcare", "llmIndustryConfidence" : 0.95 }, { "id" : "6a305e6cf292fd229cf7070e", "title" : "Quality Assurance Auditor", "company" : "PSI CRO", "industry" : "Pharmaceuticals", "salary" : { "min" : 7500000.0, "max" : 1.05E7, "currency" : "JPY", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for a Mid-level Quality Assurance Auditor in Tokyo, Japan, adjusted for the healthcare industry and full-time employment." }, "location" : "Tokyo, Tokyo, JP", "locations" : [ "Tokyo", "Tokyo", "JP" ], "timestamp" : "2026-06-16T08:36:03.159", "fingerprint" : "62e715826a03be72432b0a405fdc04cc317f07ec8827237403263e7120a3b8d7", "source" : "smartrecruiters", "language" : "en", "applyUrl" : "https://jobs.smartrecruiters.com/PSICRO/744000132166364-quality-assurance-auditor", "externalId" : "744000132166364", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Tokyo", "state" : "Tokyo", "country" : "Japan", "continent" : "as", "source" : "llm", "workMode" : "onsite", "geo" : "35.6812546,139.7667060" } ], "locationText" : "Tokyo, Tokyo, Japan", "employmentType" : "FULL_TIME", "minimumEducation" : "Bachelor's", "experienceMinYears" : 5, "experienceMaxYears" : 10, "experienceLevel" : "mid", "technicalSkills" : "MS Office", "professionalSkills" : [ "Quality management", "Quality assurance", "Study audits", "Vendor qualification", "Internal systems audits", "Process audits" ], "hardSkillsList" : [ "QA study audits", "Internal systems audits", "Internal locations audits", "Vendor qualification audits", "Internal process audits" ], "softSkillsList" : [ "Presentation", "Customer-service", "Team building", "Leadership", "Organizational skills" ], "hardSkills" : [ { "name" : "QA study audits", "level" : "intermediate" }, { "name" : "Internal systems audits", "level" : "intermediate" }, { "name" : "Internal locations audits", "level" : "intermediate" }, { "name" : "Vendor qualification audits", "level" : "intermediate" }, { "name" : "Internal process audits", "level" : "intermediate" } ], "softSkills" : [ { "name" : "Presentation" }, { "name" : "Customer-service" }, { "name" : "Team building" }, { "name" : "Leadership" }, { "name" : "Organizational skills" } ], "majorSkills" : "Quality assurance, Study audits, Vendor qualification", "majorSkillsList" : [ "Quality assurance", "Study audits", "Vendor qualification" ], "coreResponsibilities" : "Serve as communication point to project teams, Follow up with project teams, Liaise with PSI divisions, Liaise with vendors, Serve as communication point for Operations staff, Serve as communication point for clients, Prepare QA study audits, Conduct QA study audits, Report QA study audits, Assemble QA study audits documentation, Participate in preparation of internal systems audits, Participate in conduct of internal systems audits, Participate in reporting of internal systems audits, Participate in preparation of internal locations audits, Participate in conduct of internal locations audits, Participate in reporting of internal locations audits, Participate in preparation of vendor qualification audits, Participate in conduct of vendor qualification audits, Participate in reporting of vendor qualification audits, Participate in preparation of internal process audits, Participate in conduct of internal process audits, Participate in reporting of internal process audits, Train PSI Operations staff", "teamCollaboration" : "Project teams; PSI divisions; PSI operational offices; Vendors; PSI Operations staff; PSI clients", "domainKnowledge" : "Quality management topics; Contract Research Organization", "domainList" : [ "quality-management-topics", "contract-research-organization" ], "domainSpecificKnowledge" : [ "Quality management topics", "Contract Research Organization" ], "toolProficiency" : [ "MS Project" ], "communicationSkills" : "Presentation skills", "documentationSkills" : "Assemble QA study audits documentation", "qualificationsBasic" : "3 years in quality management, 2 to 3 supervised QA audits, Full working proficiency in English, Full working proficiency in Japanese", "qualificationsPreferred" : "Bachelor or above degree in life science, MS Office applications proficiency, MS Project proficiency", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "PSI is a leading Contract Research Organization with 30 years in the industry offering a perfect balance between stability and innovation to both clients and employees. We focus on delivering quality and on-time services across a variety of therapeutic indications. SCOPE OF RESPONSIBILITIES 1. Communication 1.1. Serves as the communication point to project teams with regard to quality assurance (QA) study audits 1.2. Follows up with project teams with regard to resolution of PSI QA study audits 1.3. Liaises with PSI divisions/operational offices with regard to internal systems, process, and location audits 1.4. Liaises with vendors with regard to vendor qualification audits 1.5. Serves as the communication point for PSI Operations staff with regard to issues related to quality assurance 1.6. Serves as the communication point for PSI clients with regard to QA audits contracted to PSI 2. Planning and scheduling 2.1. Prepares, conducts, and reports QA study audits 2.2. Assembles QA study audits documentation 2.3. Takes part in the preparation, conduct, and reporting of internal systems audits 2.4. Takes part in the preparation, conduct, and reporting of internal locations audits 2.5. Takes part in the preparation, conduct, and reporting of vendor qualification audits 2.6. Takes part in the preparation, conduct, and reporting of internal process audits 3. Training 3.1. Trains PSI Operations staff in quality management topics ## Qualifications 1. Bachelor or above degree in life science or an equivalent combination of education 2. Minimum of 3 years in quality management or quality assurance, 2 to 3 supervised QA audits is a must 3. Full working proficiency in English and Japanese 4. Communication, presentation, and customer-service skills 5. Team building, leadership and organizational skills 6. Proficiency in MS Office applications, including MS Project ## Additional Information Make the right call and take your career to a whole new level. Join the company that focuses on its people and invests in their professional development and success.", "summaryDescription" : "Quality Assurance Auditor at PSI CRO. Skills: Quality assurance, Study audits, Vendor qualification. Serve as communication point to project teams. Follow up with project teams", "postedDate" : "2026-06-15T10:33:18.000", "createdAt" : "2026-06-16T00:16:43.156", "lastUpdated" : "2026-06-16T08:36:03.159", "ingestTimestamp" : "2026-06-16T00:16:43.156", "mongoId" : "6a305e6cf292fd229cf7070e", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryBand" : "200k+", "hasSalary" : false, "allSkills" : [ "QA study audits", "Internal systems audits", "Internal locations audits", "Vendor qualification audits", "Internal process audits", "Presentation", "Customer-service", "Team building", "Leadership", "Organizational skills", "Quality assurance", "Study audits", "Vendor qualification", "Quality management", "Process audits", "Quality management topics", "Contract Research Organization", "MS Project" ], "skillCount" : 18, "jobCategory" : "operations", "jobSubcategory" : "general", "seniorityLevel" : "Mid", "dataQualityScore" : 1.0, "_score" : 659.50964, "daysOld" : 0, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Healthcare", "llmIndustryConfidence" : 0.95 }, { "id" : "6a306073f292fd229cf8dc7b", "title" : "Pavement Condition Index (PCI) Survey & Annotation Specialist – Freelance AI Trainer Project", "company" : "Meridial", "salary" : { "min" : 6.0, "max" : 20.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "Worldwide - Remote", "locations" : [ "Worldwide - Remote" ], "timestamp" : "2026-06-16T07:21:32.621", "fingerprint" : "e2ee0dd398fa5552aa2631a45c5b7b6922bb62d45e8289fbf952a320331c64ab", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.eu.greenhouse.io/agency/jobs/4781823101", "externalId" : "4781823101", "industryFunction" : { }, "structuredLocations" : [ { "workMode" : "remote" } ], "locationText" : "", "employmentType" : "CONTRACT", "hardSkillsList" : [ "Pavement condition assessment", "Pavement distress identification" ], "hardSkills" : [ { "name" : "Pavement condition assessment", "level" : "intermediate" }, { "name" : "Pavement distress identification", "level" : "intermediate" } ], "majorSkills" : "PCI surveys, Roadway inspection", "majorSkillsList" : [ "PCI surveys", "Roadway inspection" ], "coreResponsibilities" : "Review roadway images, Review inspection records, Review AI-generated outputs, Determine accuracy of distress types, Determine accuracy of severity levels, Determine accuracy of condition assessments, Annotate pavement features, Validate pavement features, Annotate pavement distress patterns, Validate pavement distress patterns, Analyze roadway imagery, Analyze survey excerpts, Validate AI-generated classifications, Provide written feedback, Clarify inspector distinctions, Clarify sampling unit evaluation, Clarify real-world condition influence", "domainKnowledge" : "Pavement Condition Index (PCI); Pavement distress types; Pavement severity levels; Pavement condition ratings; Cracking; Rutting; Spalling; Patching; Weathering", "domainList" : [ "pavement-condition-index-pci", "pavement-distress-types", "pavement-severity-levels", "pavement-condition-ratings", "cracking", "rutting", "spalling", "patching", "weathering" ], "domainSpecificKnowledge" : [ "Pavement Condition Index (PCI)", "Pavement distress types", "Pavement severity levels", "Pavement condition ratings", "Cracking", "Rutting", "Spalling", "Patching", "Weathering" ], "communicationSkills" : "Written feedback; Structured explanation", "qualificationsBasic" : "Familiarity with PCI surveys, Attention to visual detail, Ability to apply distress definitions, Ability to apply rating criteria, Clear written communication, Ability to explain inspection reasoning, Ability to explain classification decisions", "qualificationsPreferred" : "Experience working with inspection imagery, Experience with condition reports, Experience with roadway maintenance planning", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Are you a pavement professional or roadway inspector with hands-on experience conducting or interpreting Pavement Condition Index (PCI) surveys? Advanced AI systems are being developed to understand roadway conditions, interpret inspection imagery, and support infrastructure maintenance planning. High-quality training data based on real PCI survey practices is essential to ensure these systems accurately recognize pavement distress and condition ratings. This project relies on your expertise to help train and evaluate next-generation AI models. In this project, you will focus specifically on PCI-related workflows and annotation tasks. You will review roadway images, inspection records, and AI-generated outputs to determine whether pavement distress types, severity levels, and condition assessments are accurate and consistent with PCI methodology. A central part of the work involves annotating or validating pavement features and distress patterns so models can better learn to identify cracking, rutting, spalling, patching, weathering, and other common pavement conditions. On a typical day, you may analyze roadway imagery or survey excerpts, validate AI-generated classifications of pavement distress, and provide written feedback explaining where models misidentify features or misinterpret severity levels. You may also help clarify how inspectors distinguish between similar distress categories, how sampling units are evaluated, and how real-world field conditions influence PCI scoring and reporting. This project is well suited for pavement engineers, civil or transportation engineers, DOT specialists, highway inspectors, and other professionals who have conducted or reviewed PCI surveys or pavement condition assessments. Experience working with inspection imagery, condition reports, or roadway maintenance planning is highly valuable. Clear written communication and the ability to explain inspection reasoning and classification decisions in a structured way are essential. No prior AI or technical background is required. Training and project guidelines will be provided. What matters most is your familiarity with PCI surveys, attention to visual detail, and ability to consistently apply pavement distress definitions and rating criteria. We offer a pay range of $6 - $20 per hour, with the exact rate determined after evaluating your experience, expertise, and geographic location. Final offer amounts may vary from the pay range listed above. As a contractor, you will supply a secure computer and high-speed internet; company-sponsored benefits such as health insurance and PTO do not apply. Job title: Pavement Condition Index (PCI) Survey & Annotation Specialist – Freelance AI Trainer Project Employment type: Freelance / Contract Workplace type: Remote", "summaryDescription" : "Pavement Condition Index (PCI) Survey & Annotation Specialist – Freelance AI Trainer Project at Meridial. Skills: PCI surveys, Roadway inspection. Review roadway images. Review inspection records", "postedDate" : "2026-06-15T23:23:33.442", "createdAt" : "2026-06-15T23:23:33.442", "lastUpdated" : "2026-06-16T07:21:32.621", "ingestTimestamp" : "2026-06-15T23:23:33.442", "mongoId" : "6a306073f292fd229cf8dc7b", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 12480, "salaryAnnualMax" : 41600, "salaryBand" : "<30k", "hasSalary" : true, "allSkills" : [ "Pavement condition assessment", "Pavement distress identification", "PCI surveys", "Roadway inspection", "Pavement Condition Index (PCI)", "Pavement distress types", "Pavement severity levels", "Pavement condition ratings", "Cracking", "Rutting", "Spalling", "Patching", "Weathering" ], "skillCount" : 13, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 658.87396, "daysOld" : 0, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.92 }, { "id" : "6a306073f292fd229cf8dc79", "title" : "Pavement Condition Index (PCI) Survey & Annotation Specialist – Freelance AI Trainer Project", "company" : "Meridial", "salary" : { "min" : 6.0, "max" : 20.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "Worldwide - Remote", "locations" : [ "Worldwide - Remote" ], "timestamp" : "2026-06-16T06:46:20.672", "fingerprint" : "76732f1da2ed3bcd49f65042a09b5bd2fda9e500784faa3ced4b805762bb45a8", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.eu.greenhouse.io/agency/jobs/4781825101", "externalId" : "4781825101", "industryFunction" : { }, "structuredLocations" : [ { "workMode" : "remote" } ], "locationText" : "", "employmentType" : "CONTRACT", "hardSkillsList" : [ "PCI surveys", "Pavement condition assessment" ], "hardSkills" : [ { "name" : "PCI surveys", "level" : "intermediate" }, { "name" : "Pavement condition assessment", "level" : "intermediate" } ], "majorSkills" : "PCI surveys, Pavement condition assessment", "majorSkillsList" : [ "PCI surveys", "Pavement condition assessment" ], "coreResponsibilities" : "Review roadway images, Review inspection records, Review AI-generated outputs, Determine accuracy of distress types, Determine accuracy of severity levels, Determine accuracy of condition assessments, Annotate pavement features, Validate pavement features, Annotate distress patterns, Validate distress patterns, Analyze roadway imagery, Analyze survey excerpts, Validate AI-generated classifications, Provide written feedback, Explain where models misidentify features, Explain where models misinterpret severity, Clarify inspector distinctions between categories, Clarify how sampling units are evaluated, Clarify how field conditions influence scoring, Clarify how field conditions influence reporting", "domainKnowledge" : "Pavement Condition Index (PCI); Pavement distress types; Pavement severity levels; Pavement condition ratings; Cracking; Rutting; Spalling; Patching; Weathering; Roadway conditions; Infrastructure maintenance planning", "domainList" : [ "pavement-condition-index-pci", "pavement-distress-types", "pavement-severity-levels", "pavement-condition-ratings", "cracking", "rutting", "spalling", "patching", "weathering", "roadway-conditions", "infrastructure-maintenance-planning" ], "domainSpecificKnowledge" : [ "Pavement Condition Index (PCI)", "Pavement distress types", "Pavement severity levels", "Pavement condition ratings", "Cracking", "Rutting", "Spalling", "Patching", "Weathering", "Roadway conditions", "Infrastructure maintenance planning" ], "communicationSkills" : "Written communication; Explain inspection reasoning; Explain classification decisions", "qualificationsBasic" : "Familiarity with PCI surveys, Attention to visual detail, Ability to apply distress definitions, Ability to apply rating criteria", "qualificationsPreferred" : "Experience with inspection imagery, Experience with condition reports, Experience with roadway maintenance planning", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Are you a pavement professional or roadway inspector with hands-on experience conducting or interpreting Pavement Condition Index (PCI) surveys? Advanced AI systems are being developed to understand roadway conditions, interpret inspection imagery, and support infrastructure maintenance planning. High-quality training data based on real PCI survey practices is essential to ensure these systems accurately recognize pavement distress and condition ratings. This project relies on your expertise to help train and evaluate next-generation AI models. In this project, you will focus specifically on PCI-related workflows and annotation tasks. You will review roadway images, inspection records, and AI-generated outputs to determine whether pavement distress types, severity levels, and condition assessments are accurate and consistent with PCI methodology. A central part of the work involves annotating or validating pavement features and distress patterns so models can better learn to identify cracking, rutting, spalling, patching, weathering, and other common pavement conditions. On a typical day, you may analyze roadway imagery or survey excerpts, validate AI-generated classifications of pavement distress, and provide written feedback explaining where models misidentify features or misinterpret severity levels. You may also help clarify how inspectors distinguish between similar distress categories, how sampling units are evaluated, and how real-world field conditions influence PCI scoring and reporting. This project is well suited for pavement engineers, civil or transportation engineers, DOT specialists, highway inspectors, and other professionals who have conducted or reviewed PCI surveys or pavement condition assessments. Experience working with inspection imagery, condition reports, or roadway maintenance planning is highly valuable. Clear written communication and the ability to explain inspection reasoning and classification decisions in a structured way are essential. No prior AI or technical background is required. Training and project guidelines will be provided. What matters most is your familiarity with PCI surveys, attention to visual detail, and ability to consistently apply pavement distress definitions and rating criteria. We offer a pay range of $6 - $20 per hour, with the exact rate determined after evaluating your experience, expertise, and geographic location. Final offer amounts may vary from the pay range listed above. As a contractor, you will supply a secure computer and high-speed internet; company-sponsored benefits such as health insurance and PTO do not apply. Job title: Pavement Condition Index (PCI) Survey & Annotation Specialist – Freelance AI Trainer Project Employment type: Freelance / Contract Workplace type: Remote", "summaryDescription" : "Pavement Condition Index (PCI) Survey & Annotation Specialist – Freelance AI Trainer Project at Meridial. Skills: PCI surveys, Pavement condition assessment. Review roadway images. Review inspection records", "postedDate" : "2026-06-15T23:22:01.603", "createdAt" : "2026-06-15T23:22:01.603", "lastUpdated" : "2026-06-16T06:46:20.672", "ingestTimestamp" : "2026-06-15T23:22:01.603", "mongoId" : "6a306073f292fd229cf8dc79", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 12480, "salaryAnnualMax" : 41600, "salaryBand" : "<30k", "hasSalary" : true, "allSkills" : [ "PCI surveys", "Pavement condition assessment", "Pavement Condition Index (PCI)", "Pavement distress types", "Pavement severity levels", "Pavement condition ratings", "Cracking", "Rutting", "Spalling", "Patching", "Weathering", "Roadway conditions", "Infrastructure maintenance planning" ], "skillCount" : 13, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 657.76733, "daysOld" : 0, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.92 }, { "id" : "6a306073f292fd229cf8dc7c", "title" : "Pavement Condition Index (PCI) Survey & Annotation Specialist – Freelance AI Trainer Project", "company" : "Meridial", "salary" : { "min" : 6.0, "max" : 20.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "Worldwide - Remote", "locations" : [ "Worldwide - Remote" ], "timestamp" : "2026-06-16T07:35:33.194", "fingerprint" : "81c01d0754615f03b79e3fba0704e37c572c4439b7d4df2c90a12fbf27058e12", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.eu.greenhouse.io/agency/jobs/4781818101", "externalId" : "4781818101", "industryFunction" : { }, "structuredLocations" : [ { "workMode" : "remote" } ], "locationText" : "", "employmentType" : "CONTRACT", "hardSkillsList" : [ "Pavement condition assessment", "Pavement distress identification", "Pavement rating criteria" ], "hardSkills" : [ { "name" : "Pavement condition assessment", "level" : "intermediate" }, { "name" : "Pavement distress identification", "level" : "intermediate" }, { "name" : "Pavement rating criteria", "level" : "intermediate" } ], "majorSkills" : "PCI surveys, Pavement distress, Condition assessment", "majorSkillsList" : [ "PCI surveys", "Pavement distress", "Condition assessment" ], "coreResponsibilities" : "Review roadway images, Review inspection records, Review AI-generated outputs, Determine accuracy of distress types, Determine accuracy of severity levels, Determine accuracy of condition assessments, Annotate pavement features, Validate pavement features, Annotate distress patterns, Validate distress patterns, Analyze roadway imagery, Analyze survey excerpts, Validate AI-generated classifications, Provide written feedback, Clarify inspector distinctions, Clarify sampling unit evaluation, Clarify field condition influence", "domainKnowledge" : "Pavement Condition Index (PCI); Pavement distress types; Pavement severity levels; Pavement condition ratings; Roadway imagery analysis; Infrastructure maintenance planning", "domainList" : [ "pavement-condition-index-pci", "pavement-distress-types", "pavement-severity-levels", "pavement-condition-ratings", "roadway-imagery-analysis", "infrastructure-maintenance-planning" ], "domainSpecificKnowledge" : [ "Pavement Condition Index (PCI)", "Pavement distress types", "Pavement severity levels", "Pavement condition ratings", "Roadway imagery analysis", "Infrastructure maintenance planning" ], "communicationSkills" : "Written feedback; Explain inspection reasoning; Explain classification decisions", "qualificationsBasic" : "Familiarity with PCI surveys, Attention to visual detail, Ability to apply distress definitions, Ability to apply rating criteria", "qualificationsPreferred" : "Experience with inspection imagery, Experience with condition reports, Experience with roadway maintenance planning", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Are you a pavement professional or roadway inspector with hands-on experience conducting or interpreting Pavement Condition Index (PCI) surveys? Advanced AI systems are being developed to understand roadway conditions, interpret inspection imagery, and support infrastructure maintenance planning. High-quality training data based on real PCI survey practices is essential to ensure these systems accurately recognize pavement distress and condition ratings. This project relies on your expertise to help train and evaluate next-generation AI models. In this project, you will focus specifically on PCI-related workflows and annotation tasks. You will review roadway images, inspection records, and AI-generated outputs to determine whether pavement distress types, severity levels, and condition assessments are accurate and consistent with PCI methodology. A central part of the work involves annotating or validating pavement features and distress patterns so models can better learn to identify cracking, rutting, spalling, patching, weathering, and other common pavement conditions. On a typical day, you may analyze roadway imagery or survey excerpts, validate AI-generated classifications of pavement distress, and provide written feedback explaining where models misidentify features or misinterpret severity levels. You may also help clarify how inspectors distinguish between similar distress categories, how sampling units are evaluated, and how real-world field conditions influence PCI scoring and reporting. This project is well suited for pavement engineers, civil or transportation engineers, DOT specialists, highway inspectors, and other professionals who have conducted or reviewed PCI surveys or pavement condition assessments. Experience working with inspection imagery, condition reports, or roadway maintenance planning is highly valuable. Clear written communication and the ability to explain inspection reasoning and classification decisions in a structured way are essential. No prior AI or technical background is required. Training and project guidelines will be provided. What matters most is your familiarity with PCI surveys, attention to visual detail, and ability to consistently apply pavement distress definitions and rating criteria. We offer a pay range of $6 - $20 per hour, with the exact rate determined after evaluating your experience, expertise, and geographic location. Final offer amounts may vary from the pay range listed above. As a contractor, you will supply a secure computer and high-speed internet; company-sponsored benefits such as health insurance and PTO do not apply. Job title: Pavement Condition Index (PCI) Survey & Annotation Specialist – Freelance AI Trainer Project Employment type: Freelance / Contract Workplace type: Remote", "summaryDescription" : "Pavement Condition Index (PCI) Survey & Annotation Specialist – Freelance AI Trainer Project at Meridial. Skills: PCI surveys, Pavement distress, Condition assessment. Review roadway images. Review inspection records", "postedDate" : "2026-06-15T23:25:02.616", "createdAt" : "2026-06-15T23:25:02.616", "lastUpdated" : "2026-06-16T07:35:33.194", "ingestTimestamp" : "2026-06-15T23:25:02.616", "mongoId" : "6a306073f292fd229cf8dc7c", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 12480, "salaryAnnualMax" : 41600, "salaryBand" : "<30k", "hasSalary" : true, "allSkills" : [ "Pavement condition assessment", "Pavement distress identification", "Pavement rating criteria", "PCI surveys", "Pavement distress", "Condition assessment", "Pavement Condition Index (PCI)", "Pavement distress types", "Pavement severity levels", "Pavement condition ratings", "Roadway imagery analysis", "Infrastructure maintenance planning" ], "skillCount" : 12, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 656.89716, "daysOld" : 0, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.92 }, { "id" : "6a306073f292fd229cf8dc77", "title" : "Pavement Condition Index (PCI) Survey & Annotation Specialist – Freelance AI Trainer Project", "company" : "Meridial", "salary" : { "min" : 6.0, "max" : 20.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "Worldwide - Remote", "locations" : [ "Worldwide - Remote" ], "timestamp" : "2026-06-16T07:24:40.323", "fingerprint" : "d8b2f9b58bd8a069f5192092c4349eccb8a5307ec082aea6e61391486e695cc6", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.eu.greenhouse.io/agency/jobs/4781816101", "externalId" : "4781816101", "industryFunction" : { }, "structuredLocations" : [ { "workMode" : "remote" } ], "locationText" : "", "employmentType" : "CONTRACT", "hardSkillsList" : [ "PCI surveys", "Pavement distress identification", "Condition assessment" ], "hardSkills" : [ { "name" : "PCI surveys", "level" : "intermediate" }, { "name" : "Pavement distress identification", "level" : "intermediate" }, { "name" : "Condition assessment", "level" : "intermediate" } ], "majorSkills" : "PCI surveys, Pavement condition assessment, AI model training", "majorSkillsList" : [ "PCI surveys", "Pavement condition assessment", "AI model training" ], "coreResponsibilities" : "Review roadway images, Review inspection records, Review AI-generated outputs, Determine accuracy of distress types, Determine accuracy of severity levels, Determine accuracy of condition assessments, Annotate pavement features, Validate pavement features, Annotate distress patterns, Validate distress patterns, Analyze roadway imagery, Analyze survey excerpts, Validate AI-generated classifications, Provide written feedback, Clarify inspector distinctions, Clarify sampling unit evaluation, Clarify field condition influence", "domainKnowledge" : "Pavement Condition Index (PCI); Pavement distress types; Pavement severity levels; Pavement condition ratings; Roadway inspection; Infrastructure maintenance planning", "domainList" : [ "pavement-condition-index-pci", "pavement-distress-types", "pavement-severity-levels", "pavement-condition-ratings", "roadway-inspection", "infrastructure-maintenance-planning" ], "domainSpecificKnowledge" : [ "Pavement Condition Index (PCI)", "Pavement distress types", "Pavement severity levels", "Pavement condition ratings", "Roadway inspection", "Infrastructure maintenance planning" ], "communicationSkills" : "Written feedback; Explain inspection reasoning; Explain classification decisions", "qualificationsBasic" : "Familiarity with PCI surveys, Attention to visual detail, Ability to apply distress definitions, Ability to apply rating criteria", "qualificationsPreferred" : "Experience with inspection imagery, Experience with condition reports, Experience with roadway maintenance planning", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Are you a pavement professional or roadway inspector with hands-on experience conducting or interpreting Pavement Condition Index (PCI) surveys? Advanced AI systems are being developed to understand roadway conditions, interpret inspection imagery, and support infrastructure maintenance planning. High-quality training data based on real PCI survey practices is essential to ensure these systems accurately recognize pavement distress and condition ratings. This project relies on your expertise to help train and evaluate next-generation AI models. In this project, you will focus specifically on PCI-related workflows and annotation tasks. You will review roadway images, inspection records, and AI-generated outputs to determine whether pavement distress types, severity levels, and condition assessments are accurate and consistent with PCI methodology. A central part of the work involves annotating or validating pavement features and distress patterns so models can better learn to identify cracking, rutting, spalling, patching, weathering, and other common pavement conditions. On a typical day, you may analyze roadway imagery or survey excerpts, validate AI-generated classifications of pavement distress, and provide written feedback explaining where models misidentify features or misinterpret severity levels. You may also help clarify how inspectors distinguish between similar distress categories, how sampling units are evaluated, and how real-world field conditions influence PCI scoring and reporting. This project is well suited for pavement engineers, civil or transportation engineers, DOT specialists, highway inspectors, and other professionals who have conducted or reviewed PCI surveys or pavement condition assessments. Experience working with inspection imagery, condition reports, or roadway maintenance planning is highly valuable. Clear written communication and the ability to explain inspection reasoning and classification decisions in a structured way are essential. No prior AI or technical background is required. Training and project guidelines will be provided. What matters most is your familiarity with PCI surveys, attention to visual detail, and ability to consistently apply pavement distress definitions and rating criteria. We offer a pay range of $6 - $20 per hour, with the exact rate determined after evaluating your experience, expertise, and geographic location. Final offer amounts may vary from the pay range listed above. As a contractor, you will supply a secure computer and high-speed internet; company-sponsored benefits such as health insurance and PTO do not apply. Job title: Pavement Condition Index (PCI) Survey & Annotation Specialist – Freelance AI Trainer Project Employment type: Freelance / Contract Workplace type: Remote", "summaryDescription" : "Pavement Condition Index (PCI) Survey & Annotation Specialist – Freelance AI Trainer Project at Meridial. Skills: PCI surveys, Pavement condition assessment, AI model training. Review roadway images. Review inspection records", "postedDate" : "2026-06-15T23:28:37.251", "createdAt" : "2026-06-15T23:28:37.251", "lastUpdated" : "2026-06-16T07:24:40.323", "ingestTimestamp" : "2026-06-15T23:28:37.251", "mongoId" : "6a306073f292fd229cf8dc77", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 12480, "salaryAnnualMax" : 41600, "salaryBand" : "<30k", "hasSalary" : true, "allSkills" : [ "PCI surveys", "Pavement distress identification", "Condition assessment", "Pavement condition assessment", "AI model training", "Pavement Condition Index (PCI)", "Pavement distress types", "Pavement severity levels", "Pavement condition ratings", "Roadway inspection", "Infrastructure maintenance planning" ], "skillCount" : 11, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 655.14386, "daysOld" : 0, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.92 }, { "id" : "6a306073f292fd229cf8dc76", "title" : "Pavement Condition Index (PCI) Survey & Annotation Specialist – Freelance AI Trainer Project", "company" : "Meridial", "salary" : { "min" : 6.0, "max" : 20.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "Worldwide - Remote", "locations" : [ "Worldwide - Remote" ], "timestamp" : "2026-06-16T08:57:41.637", "fingerprint" : "bbcfc9cafcb5de707855b6640efd50e95737f9c9a74c303e603eae603595f87d", "source" : "greenhouse", "language" : "en", "applyUrl" : "https://job-boards.eu.greenhouse.io/agency/jobs/4781817101", "externalId" : "4781817101", "industryFunction" : { }, "structuredLocations" : [ { "workMode" : "remote" } ], "locationText" : "", "employmentType" : "CONTRACT", "hardSkillsList" : [ "PCI surveys", "Pavement distress identification", "Condition assessment" ], "hardSkills" : [ { "name" : "PCI surveys", "level" : "intermediate" }, { "name" : "Pavement distress identification", "level" : "intermediate" }, { "name" : "Condition assessment", "level" : "intermediate" } ], "majorSkills" : "PCI surveys, Pavement condition assessment, AI model training", "majorSkillsList" : [ "PCI surveys", "Pavement condition assessment", "AI model training" ], "coreResponsibilities" : "Review roadway images, Review inspection records, Review AI-generated outputs, Determine accuracy of distress types, Determine accuracy of severity levels, Determine accuracy of condition assessments, Annotate pavement features, Validate pavement features, Annotate distress patterns, Validate distress patterns, Analyze roadway imagery, Analyze survey excerpts, Validate AI-generated classifications, Provide written feedback, Clarify inspector distinctions, Clarify sampling unit evaluation, Clarify real-world condition influence", "domainKnowledge" : "Pavement Condition Index (PCI); Pavement distress types; Pavement severity levels; Pavement condition ratings; Roadway inspection; Infrastructure maintenance planning", "domainList" : [ "pavement-condition-index-pci", "pavement-distress-types", "pavement-severity-levels", "pavement-condition-ratings", "roadway-inspection", "infrastructure-maintenance-planning" ], "domainSpecificKnowledge" : [ "Pavement Condition Index (PCI)", "Pavement distress types", "Pavement severity levels", "Pavement condition ratings", "Roadway inspection", "Infrastructure maintenance planning" ], "communicationSkills" : "Written feedback; Explain inspection reasoning; Explain classification decisions", "qualificationsBasic" : "Familiarity with PCI surveys, Attention to visual detail, Ability to apply distress definitions, Ability to apply rating criteria", "qualificationsPreferred" : "Experience with inspection imagery, Experience with condition reports, Experience with roadway maintenance planning", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "Are you a pavement professional or roadway inspector with hands-on experience conducting or interpreting Pavement Condition Index (PCI) surveys? Advanced AI systems are being developed to understand roadway conditions, interpret inspection imagery, and support infrastructure maintenance planning. High-quality training data based on real PCI survey practices is essential to ensure these systems accurately recognize pavement distress and condition ratings. This project relies on your expertise to help train and evaluate next-generation AI models. In this project, you will focus specifically on PCI-related workflows and annotation tasks. You will review roadway images, inspection records, and AI-generated outputs to determine whether pavement distress types, severity levels, and condition assessments are accurate and consistent with PCI methodology. A central part of the work involves annotating or validating pavement features and distress patterns so models can better learn to identify cracking, rutting, spalling, patching, weathering, and other common pavement conditions. On a typical day, you may analyze roadway imagery or survey excerpts, validate AI-generated classifications of pavement distress, and provide written feedback explaining where models misidentify features or misinterpret severity levels. You may also help clarify how inspectors distinguish between similar distress categories, how sampling units are evaluated, and how real-world field conditions influence PCI scoring and reporting. This project is well suited for pavement engineers, civil or transportation engineers, DOT specialists, highway inspectors, and other professionals who have conducted or reviewed PCI surveys or pavement condition assessments. Experience working with inspection imagery, condition reports, or roadway maintenance planning is highly valuable. Clear written communication and the ability to explain inspection reasoning and classification decisions in a structured way are essential. No prior AI or technical background is required. Training and project guidelines will be provided. What matters most is your familiarity with PCI surveys, attention to visual detail, and ability to consistently apply pavement distress definitions and rating criteria. We offer a pay range of $6 - $20 per hour, with the exact rate determined after evaluating your experience, expertise, and geographic location. Final offer amounts may vary from the pay range listed above. As a contractor, you will supply a secure computer and high-speed internet; company-sponsored benefits such as health insurance and PTO do not apply. Job title: Pavement Condition Index (PCI) Survey & Annotation Specialist – Freelance AI Trainer Project Employment type: Freelance / Contract Workplace type: Remote", "summaryDescription" : "Pavement Condition Index (PCI) Survey & Annotation Specialist – Freelance AI Trainer Project at Meridial. Skills: PCI surveys, Pavement condition assessment, AI model training. Review roadway images. Review inspection records", "postedDate" : "2026-06-15T23:27:14.421", "createdAt" : "2026-06-15T23:27:14.421", "lastUpdated" : "2026-06-16T08:57:41.637", "ingestTimestamp" : "2026-06-15T23:27:14.421", "mongoId" : "6a306073f292fd229cf8dc76", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 12480, "salaryAnnualMax" : 41600, "salaryBand" : "<30k", "hasSalary" : true, "allSkills" : [ "PCI surveys", "Pavement distress identification", "Condition assessment", "Pavement condition assessment", "AI model training", "Pavement Condition Index (PCI)", "Pavement distress types", "Pavement severity levels", "Pavement condition ratings", "Roadway inspection", "Infrastructure maintenance planning" ], "skillCount" : 11, "jobCategory" : "engineering", "jobSubcategory" : "data", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 655.08276, "daysOld" : 0, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.92 }, { "id" : "6a306d86f292fd229cfd8e7c", "title" : "FSQ Auditor", "company" : "Jack Link's Protein Snacks", "industry" : "Consumer Goods", "salary" : { "min" : 75000.0, "max" : 105000.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for a Mid-level FSQ Auditor in Minong, Wisconsin, adjusted for the Food & Beverage industry." }, "location" : "Minong, WI, United States", "locations" : [ "Minong", "WI", "United States" ], "timestamp" : "2026-06-16T09:42:22.905", "fingerprint" : "ff793e3594df2084cf88b42a42859c19279aa42d0b1f03951fe2f23ced81d545", "source" : "smartrecruiters", "language" : "en", "applyUrl" : "https://jobs.smartrecruiters.com/JackLinksProteinSnacks/744000132287174-fsq-auditor", "externalId" : "744000132287174", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Minong", "state" : "Wisconsin", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "46.1003414,-91.8238136" } ], "locationText" : "Minong, Wisconsin, United States", "employmentType" : "FULL_TIME", "experienceLevel" : "not-applicable", "remoteFriendly" : false, "rawDescription" : "At Link Snacks , we show up hungry, roll up our sleeves, plaid or otherwise, and dive straight into the hard work. We don’t hide from challenges; we hunt them down. We push harder, move faster, and take the kinds of big swings that built this business in the North Woods of Wisconsin four decades ago. We're everyday people who deliver extraordinary results. Innovation, creativity, and urgency aren’t buzzwords, they’re expectations. And just like the real protein snacks we make across our global house of brands—[Jack Link’s®](https://nam02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.jacklinks.com%2F&data=05%7C02%7Cemma.gibson%40jacklinks.com%7C78c6c2e431a841c1d33108dea7c282ae%7Cc88406accc954243baaeecf33e36426b%7C1%7C0%7C639132651014944412%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=V2oFK%2Bp7SVRKcjqPZE2tDhTz7SYv1UPeWIia5wxlD%2Bo%3D&reserved=0) , [Lorissa’s Kitchen®](https://nam02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.lorissaskitchen.com%2F&data=05%7C02%7Cemma.gibson%40jacklinks.com%7C78c6c2e431a841c1d33108dea7c282ae%7Cc88406accc954243baaeecf33e36426b%7C1%7C0%7C639132651014977670%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=4PvtVZ5kKhb5lhrUa0zDB62ma%2B1ppalj9j1yyNxy78M%3D&reserved=0) , [Golden Island®](https://nam02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.goldenislandjerky.com%2F&data=05%7C02%7Cemma.gibson%40jacklinks.com%7C78c6c2e431a841c1d33108dea7c282ae%7Cc88406accc954243baaeecf33e36426b%7C1%7C0%7C639132651014994227%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=Glkoavxz2X%2FRCA%2B6Q4hW%2FsMGRtFodYD6o3YphfJHc2U%3D&reserved=0) , [BiFi®](https://nam02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.bifi.com%2Fen&data=05%7C02%7Cemma.gibson%40jacklinks.com%7C78c6c2e431a841c1d33108dea7c282ae%7Cc88406accc954243baaeecf33e36426b%7C1%7C0%7C639132651015011537%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=y98jBJUjgw8%2BY7J12IjZkCAxeHUkIjFfFCLcUAotHF0%3D&reserved=0) , and [Peperami®](https://nam02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.peperami.tv%2F&data=05%7C02%7Cemma.gibson%40jacklinks.com%7C78c6c2e431a841c1d33108dea7c282ae%7Cc88406accc954243baaeecf33e36426b%7C1%7C0%7C639132651015027090%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=maSudYWEjlvlFvBXqcaFaX56AqyfJWZw2ROsbOEsdrg%3D&reserved=0) and [KOOEE!®](https://nam02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.kooeesnacks.com.au%2F&data=05%7C02%7Cemma.gibson%40jacklinks.com%7C78c6c2e431a841c1d33108dea7c282ae%7Cc88406accc954243baaeecf33e36426b%7C1%7C0%7C639132651015041988%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=lD8PvmZrxa%2BvVVxh64TVK6ZeqkTiHmzQNd97ulmTiN8%3D&reserved=0) to name a few—there’s nothing artificial about us. What you see is exactly what you get: humble, gritty, passionate people who show up every day ready to not just be a participate in the meat snack industry that the Link Family created, but to move faster, innovate harder and keep raising the bar so high, the rest of the world needs a ladder just to see it. Dominating this space across the world takes a team. A team that runs together, succeeds together, and celebrates together. A team that’s full of relentless energy and spirit that can only be fueled by one thing: a fist full of our delicious meat snacks. Real Meat Protein. Real People. Real Results. THAT is Link Snacks. The FSQ Auditor supports the implementation of HACCP, SOP, SQF, and R&D and performs plant-wide auditing functions. This role completes documentation; ensures that product specifications are met; customer quality expectations are achieved; USDA regulations are complied with; all quality issues are identified, investigated, and addressed. Monitoring room temperatures, monitoring product temperatures, and monitoring product quality/consistency. Shift: 2nd Shift Monday - Thursday, 2:30pm to 1:00am Wage: $23.00 to $25.00 per hours ( $20.00 to $22.00 per hours PLUS $3.00 Shift Differential) DUTIES AND RESPONSIBILITIES The duties and responsibilities of this position shall consist of, but not be limited to, the following: * Perform quality inspections on random product samples to ensure samples meet company specific Quality standards. [For example: thickness, weights, lengths, count, metal detector, etc.] * Perform pre-op – depending on time, scheduling and production including first piece checks at start of shift or new product changeover. * Monitoring the overall safety of the food quality to ensure accurate product identification and recipe instructions are being followed. Answer questions regarding rejected product, damage on inbound cases and mislabeled cases. * Follow plant holds and releases. * Enter quality inspection data into electronic/written form. * Record deviations/issues into compliance tracker and inform lead/supervisor of said deviations or defects. * Process inbound and outbound supplier corrective action requests. * Perform weekly daily audits. * Prepares necessary forms and trend charts in support of kitchen or packaging inspection function. * Implement corrective actions on deviations found during quality inspections, including product loss or adjustments. * Review of documentation for accuracy and completeness. * HACCP sign off – dependent on qualifications and availability. * Review/Close out hold tags as necessary- list should be printed off daily, hold tags must be accounted for; report missing hold tags to lead/supervisor. * Verify equipment is in good condition and calibrated, as needed. * HACCP, SSOP, SQF, Prerequisite program maintenance - accuracy, completeness, implementation; aligned with review of production paperwork, verifications, calibrations, and counseling, etc. * Follow Good Manufacturing Practices (GMPs), Hazard Analysis and Critical Control Point (HACCP) training, along with company pre-requisite programs, Standard Operating Procedures (SOP’s), and Standard Sanitation Operating Procedures (SSOP’s). Observe team members to ensure compliance, coach as necessary. * Acts as a backup for daily operational checklists. * Assist Quality Control Supervisor with serious or repeated quality problems to help determine the cause and prevent further occurrences. * Maintain SQF and HACCP book, keep documents up to date. * Assist with Quality Control and Production audits. * Performs other duties and responsibilities as necessary. Safety * Perform all duties within OSHA and company safety standards; LOTO, wear PPE and maintain safety equipment. * Maintain a safe, clean and organized work area. * Immediately report all accidents and deficiencies to the lead/foreman/supervisor. * Ensure safety devices are installed and working properly. * Monitor work environment for general food safety hazards such as condensation monitoring, etc. Communication * Communicate with coworkers and leaders in an effective, timely manner. * Keep foreman/supervisor informed daily of problems incurred during the shift. * Suggest improvements; continuously improve the quality of all support activities. ## Qualifications REQUIRED EDUCATION AND EXPERIENCE Required Education : High school diploma or GED Required Experience : 1-2 years of experience and/or training in a manufacturing or food environment Preferred : * Knowledge and experience in quality systems and production * Prior knowledge of food processing equipment * Basic knowledge of supply chain, purchasing, materials and/or manufacturing * Knowledge of SAP * HACCP certification preferred, must be completed within 6 months * Ability to calibrate delicate instruments and take accurate readings on fine gauges. * College degree REQUIRED SKILLS, KNOWLEDGE and ABILITIES : * An aptitude for math is required. * Ability to work under pressure, meet deadlines and handle multiple projects simultaneously. * Superior accuracy and attention to detail. * Ability to find a solution for or to deal proactively with work-related problems * Ability to read and interpret documents such as safety rules, operating and/or maintenance instructions and procedure manuals. * Strong verbal and written communication skills in English. * Ability to carry out detailed but uninvolved written or oral instructions. * Ability to organize and prioritize in a multi-tasking environment * Ability to collaborate with remote employees and work as part of a team. * Must have exceptional time management skills. * Firm knowledge of quality assurance methods, product testing methods, standard measurements, regulatory compliance standards, and documentation procedures * Proficient in Internet navigation, Microsoft Word, Excel, PowerPoint, and Outlook. * Must demonstrate effective leadership, problem solving, presentation, and employee motivational skills. ## Additional Information PHYSICAL DEMANDS : The physical demands described here are representative of those that must be met by an employee to be successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. While performing the duties of this job the employee is regularly required to stand, walk, use hands to finger, handle, or feel, lift and/or move up to 25 pounds, and talk or hear. The employee is occasionally required to sit, and reach with hands and arms. The employee must occasionally lift and/or move up to 50 pounds. Specific vision abilities required by this job include close vision. WORK ENVIRONMENT : The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job. The work environment is a plant / office setting with varying degrees of temperatures and noise levels. Exposure to manufacturing equipment movement and wet / slippery floors. Travel may be required. Equal Employment Opportunity Employer Jack Link's is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation and gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law. E-Verify Jack Link's provides the Social Security Administration (SSA) and, if necessary, the Department of Homeland Security (DHS), with information from each new Team Member’s Form I-9 to confirm work authorization. IMPORTANT: If the Government cannot confirm that you are authorized to work, we are required to provide you written instructions and an opportunity to contact SSA and/or DHS before taking adverse action against you, including terminating your employment. We do not use E-Verify to pre-screen job applicants or to re-verify current employees and may not limit or influence the choice of documents presented for use on the Form I-9. In order to determine whether Form I-9 documentation is valid, we use E-Verify’s photo screening tool to match the photograph appearing on some permanent resident and employment authorization cards with the official U.S. Citizenship and Immigration Services’ (USCIS) photograph. If you believe that Jack Link’s has violated our responsibilities under this program or has discriminated against you during the verification process based upon your national origin or citizenship status, please call the Office of Special Counsel at 1-800-255-7688 (TDD: 1-800-237-2515). All your information will be kept confidential according to EEO guidelines.", "summaryDescription" : "FSQ Auditor at Jack Link's Protein Snacks", "postedDate" : "2026-06-15T21:18:57.000", "createdAt" : "2026-06-16T00:28:11.230", "lastUpdated" : "2026-06-16T09:42:22.905", "ingestTimestamp" : "2026-06-16T00:28:11.230", "mongoId" : "6a306d86f292fd229cfd8e7c", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 75000, "salaryAnnualMax" : 105000, "salaryBand" : "75k-100k", "hasSalary" : true, "allSkills" : [ ], "skillCount" : 0, "jobCategory" : "operations", "jobSubcategory" : "general", "seniorityLevel" : "Mid", "dataQualityScore" : 0.8888889, "_score" : 645.9492, "daysOld" : 0, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Food & Beverage", "llmIndustryConfidence" : 0.95 }, { "id" : "6a308b24f292fd229c0985bd", "title" : "Night Auditor", "company" : "Aspen Skiing Company", "industry" : "Hospitality", "salary" : { "min" : 36000.0, "max" : 45000.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for an Entry level Night Auditor in Aspen, United States, adjusted for the hospitality industry and part-time employment." }, "location" : "Aspen, CO, United States", "locations" : [ "Aspen", "CO", "United States" ], "timestamp" : "2026-06-16T11:30:21.987", "fingerprint" : "eb597d4f24ca58d7f57128c51e1776e0e90fad7451ea54b29b99a212f4882d40", "source" : "smartrecruiters", "language" : "en", "applyUrl" : "https://jobs.smartrecruiters.com/AspenSkiingCompany/744000132254189-night-auditor-limelight-aspen-full-time-or-part-time", "externalId" : "744000132254189", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Aspen", "state" : "Colorado", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "39.1911128,-106.8235600" } ], "locationText" : "Aspen, Colorado, United States", "employmentType" : "PART_TIME", "experienceMinYears" : 2, "experienceMaxYears" : 5, "experienceLevel" : "mid", "remoteFriendly" : false, "rawDescription" : "Limelight Hotels by Aspen One provide authentic and contemporary connections to their communities and the adventures that surround them. Each Limelight hotel is inspired by place, united by purpose, defined by you and set in the heart of elevated and unique locations, each carefully designed with distinctive design and purposeful functionality, created to evoke a sense of place and a point of view. Current locations include those across Colorado in Aspen, Snowmass, Denver, and Boulder (opened August 2025); Ketchum, Idaho; Mammoth, California (opened December 2025); and Charleston, South Carolina (coming in 2028). For more information, visit [www.limelighthotels.com](https://www.limelighthotels.com/) or follow @limelighthotel on X, Instagram or Facebook. Please note that all official communications from the Talent Acquisition or Human Resources team are sent from email addresses within the notifications@smartrecruiters.com, aspen.com, aspensnowmass.com, aspenhospitality.co, limelighthotels.com & thelittlenell.com domains. Position Summary The Night Auditor balances daily financial transactions, verifies guest accounts, and ensures accurate reporting for hotel operations. This role combines overnight front desk responsibilities with accounting duties, providing guest assistance while reconciling revenue, processing payments, and preparing financial reports for management. This position reports to the Front Office Manager. This position, if full time, will cover both Limelight Aspen and Snowmass locations, 2 to 3 days per week. If part time, the position will cover Limelight Aspen solely, 2 to 3 days per week. Job Posting Deadline Applications for this position will be accepted until June 21, 2026. Essential Job Functions • Act as Front Desk Receptionist, Concierge and PBX Operator during this shift • Assist with guest requests and any guest concerns or issues • Run night audit daily processing • Notate and follow up on wakeup calls from the wakeup call sheet • Roll all expected arrivals and no shows to next day • Post overnight parking charges • Perform check in and check out duties • Compile and distribute house reports, including arrivals, departure and in-house report • Sort and file daily receipts for all restaurant outlets • Make management aware of guest’s comments and complaints • Answer incoming internal and external phone calls, as well as additional PBX (phone operator) duties as needed • Other duties as assigned ## Qualifications Education and Experience Requirements • Must be at least 18 years of age • High School Diploma or high school equivalent required • 1 year of customer/ guest experience preferred Knowledge, Skills & Abilities • Basic industry terminology • Familiar with hotel fire and safety procedures, assisting in emergency and security procedures as directed by management • Knowledge of company policies and procedures • Strong problem-solving and critical-thinking skills to address guest inquiries and resolve concerns promptly • Strong attention to detail • Active listening and interpersonal skills to personalize the guest experience • Ability to act professional in a fast-paced environment • Ability to understand written and verbal English • Ability to work independently with minimal supervision • Ability to work effectively in team settings, contributing positively to group dynamics • Ability to demonstrate expertise in the technical aspect of the job • Comply with all policies and procedures for the Front Desk department • Complete tasks and projects delegated by your manager and assist other departments as business volumes and staff levels demand ## Additional Information Work Environment & Physical Demands • Ability to stand, walk or be on your feet for extended periods of time, kneeling and bending may be required • Regularly work indoors with no adverse conditions • Must be able to occasionally lift, push or pull up to 50 lbs. individually or with assistance (weight limits can vary according to position, but no more than 50) Job Benefits This position is classified as a regular full-time position eligible for the following benefits: Enrollment dates differ across the various programs. • Health, Dental and Vision Insurance Programs • Flexible Spending Account Programs • Life Insurance Programs • Paid Time Off Programs • Paid Leave Programs • 401(k) Savings Plan • Employee Ski Pass and Dependent Ski Passes • Other company perks OR This position is classified as a regular part-time position eligible for the following benefits: Enrollment dates differ across the various programs. • Paid Time Off Programs • Paid Leave Programs • Employee Ski Pass • Other company perks The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. By accepting a position with Aspen One, Aspen Snowmass, Aspen Ventures or Aspen Hospitality you acknowledge that you are able to perform the essential functions of the job with or without reasonable accommodation. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions of the job. To request accommodation during the application and interviewing process, please contact Human Resources at 970-300-7700. This job description is designed to indicate the general nature and level of work. It is not designed to contain or be interpreted as a comprehensive list of all duties, responsibilities, and qualifications required of employees assigned to this job. Duties and responsibilities may change at any time with or without notice. Aspen One is an equal opportunity employer (Minority/Female/Disabled/Veteran). At Aspen One, inclusion, equity, and diversity are fundamental to fulfilling our vision of building a better workplace and better world. From our hiring practices through the entire employee experience, we embrace and celebrate the unique experiences, perspectives and cultural backgrounds that each employee brings to the workplace. We encourage diverse points of view which allows us to develop innovative solutions to the ever-evolving world of work. Aspen One strives to foster an environment where our employees feel respected, valued and empowered, and our team members are at the forefront of helping us promote and sustain an inclusive workplace that works for all. For an overview of Aspen One Company's benefits and other compensation visit [www.aspensnowmass.com/employment/benefits-and-perks](http://www.aspensnowmass.com/employment/benefits-and-perks) Aspen One participates in E-Verify. [E-Verify & Right to Work Poster](https://rise.articulate.com/share/Czdyt_D1H8detTw7YTgvC06MJRN6tLix)", "summaryDescription" : "Night Auditor at Aspen Skiing Company", "postedDate" : "2026-06-15T17:43:22.000", "createdAt" : "2026-06-16T01:42:40.602", "lastUpdated" : "2026-06-16T11:30:21.987", "ingestTimestamp" : "2026-06-16T01:42:40.602", "mongoId" : "6a308b24f292fd229c0985bd", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 36000, "salaryAnnualMax" : 45000, "salaryBand" : "30k-50k", "hasSalary" : true, "allSkills" : [ ], "skillCount" : 0, "jobCategory" : "operations", "jobSubcategory" : "general", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 637.4462, "daysOld" : 0, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Hospitality", "llmIndustryConfidence" : 0.95 }, { "id" : "6a305119f292fd229cefa257", "title" : "Senior Community Manager", "industry" : "Marketing", "salary" : { "min" : 125000.0, "max" : 185000.0, "currency" : "EUR", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for a Senior Community Manager in Romania, adjusted for the technology industry and remote work." }, "location" : "Romania", "locations" : [ "Romania" ], "timestamp" : "2026-06-16T12:44:05.416", "fingerprint" : "58531a53f415b50a1cf4a5d7c7a223b18a68b4361d5da2c557959cb244f838cc", "source" : "lever", "language" : "en", "applyUrl" : "https://jobs.lever.co/jobgether/182e8ea1-b81f-42ed-984a-2b121de6b620/apply", "externalId" : "182e8ea1-b81f-42ed-984a-2b121de6b620", "industryFunction" : { }, "structuredLocations" : [ { "country" : "Romania", "continent" : "eu", "workMode" : "remote" } ], "locationText" : "Romania", "employmentType" : "FULL_TIME", "experienceMinYears" : 6, "experienceMaxYears" : 10, "experienceLevel" : "Senior", "professionalSkills" : [ "Ecosystem leadership", "Network-based roles", "Multi-sided platforms", "Relationship management", "Strategic expansion", "Systems-level thinking" ], "hardSkillsList" : [ "Community management", "Ecosystem dynamics", "Network effects", "Engagement metrics", "Retention metrics", "Ecosystem health metrics" ], "hardSkills" : [ { "name" : "Community management", "level" : "intermediate" }, { "name" : "Ecosystem dynamics", "level" : "intermediate" }, { "name" : "Network effects", "level" : "intermediate" }, { "name" : "Engagement metrics", "level" : "intermediate" }, { "name" : "Retention metrics", "level" : "intermediate" }, { "name" : "Ecosystem health metrics", "level" : "intermediate" } ], "majorSkills" : "Community management, Ecosystem leadership, Relationship strategies, Feedback loops", "majorSkillsList" : [ "Community management", "Ecosystem leadership", "Relationship strategies", "Feedback loops" ], "coreResponsibilities" : "Define and design ecosystem structure, Develop and maintain multi-stakeholder relationship strategies, Identify ecosystem risks, Identify sentiment shifts, Identify trust gaps, Implement structured communication strategies, Build community-to-product feedback loops, Translate ecosystem insights into actionable recommendations, Create rituals, Create recognition systems, Create narrative frameworks, Support strategic expansion efforts, Evaluate new segments, Evaluate new markets, Collaborate with leadership to improve ecosystem resilience, Collaborate with leadership to reduce fragmentation, Collaborate with leadership to increase stakeholder retention, Collaborate with leadership to increase stakeholder loyalty", "teamCollaboration" : "Multi-stakeholder relationships; Leadership collaboration; Product teams collaboration", "domainKnowledge" : "Multi-sided platforms; Marketplaces; SaaS ecosystems; Distributed networks; Network effects; Ecosystem dynamics", "domainList" : [ "multi-sided-platforms", "marketplaces", "saas-ecosystems", "distributed-networks", "network-effects", "ecosystem-dynamics" ], "domainSpecificKnowledge" : [ "Multi-sided platforms", "Marketplaces", "SaaS ecosystems", "Distributed networks", "Network effects", "Ecosystem dynamics" ], "communicationSkills" : "Structured communication; Executive communication; Community communication", "problemSolvingSkills" : "Systems-level thinking", "outcomesObjectives" : "Strengthen trust and engagement; Address ecosystem risks; Reinforce identity; Strengthen ecosystem belonging; Increase long-term stakeholder retention; Increase long-term stakeholder loyalty", "qualificationsBasic" : "6–10+ years of experience in community management, 6–10+ years of experience in ecosystem leadership, 6–10+ years of experience in network-based roles, Proven experience working with multi-sided platforms, Ability to build and maintain relationships across diverse stakeholder groups, Strategic mindset, Deep understanding of network effects, Deep understanding of ecosystem dynamics, Analytical comfort with engagement metrics, Analytical comfort with retention metrics, Analytical comfort with ecosystem health metrics, Excellent communication skills, Experience translating community insights into strategic decisions, Experience translating community insights into product-level decisions, Sense of ownership, Ability to operate independently in complex environments, High emotional intelligence, Structured, systems-level thinking", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "## Accountabilities Define and design the structure of the ecosystem, including how users, contributors, clients, and partners interact and create shared value. Develop and maintain multi-stakeholder relationship strategies that strengthen trust and engagement across all segments. Identify ecosystem risks, sentiment shifts, and trust gaps early, and implement structured communication strategies to address them. Build and refine community-to-product feedback loops, translating ecosystem insights into actionable recommendations for leadership and product teams. Create rituals, recognition systems, and narrative frameworks that reinforce identity and strengthen ecosystem belonging. Support strategic expansion efforts by evaluating new segments and markets through a community and network lens. Collaborate with leadership to improve ecosystem resilience, reduce fragmentation, and increase long-term stakeholder retention and loyalty. Requirements: 6–10+ years of experience in community management, ecosystem leadership, or network-based roles. Proven experience working with multi-sided platforms such as marketplaces, SaaS ecosystems, or distributed networks. Strong ability to build and maintain relationships across diverse stakeholder groups, including enterprise and grassroots communities. Strategic mindset with a deep understanding of network effects and ecosystem dynamics. Analytical comfort with engagement, retention, and ecosystem health metrics. Excellent communication skills, with the ability to engage at both executive and community levels. Experience translating community insights into strategic or product-level decisions. Strong sense of ownership, with the ability to operate independently in complex environments. High emotional intelligence combined with structured, systems-level thinking. Benefits: Competitive compensation aligned with experience Flexible and remote-friendly work environment Opportunity to shape a high-impact global ecosystem Strong ownership and strategic decision-making responsibility Collaborative, mission-driven, and intellectually stimulating environment Exposure to multi-stakeholder, international network dynamics Career growth in a high-level community and ecosystem leadership role Direct influence on product, strategy, and organizational direction ## Additional Information How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. #LI-CL1", "summaryDescription" : "Senior Community Manager. Skills: Community management, Ecosystem leadership, Relationship strategies, Feedback loops. Define and design ecosystem structure. Develop and maintain multi-stakeholder relationship strategies", "postedDate" : "2026-06-15T11:50:07.391", "createdAt" : "2026-06-15T11:50:07.391", "lastUpdated" : "2026-06-16T12:44:05.416", "ingestTimestamp" : "2026-06-16T03:25:15.422", "mongoId" : "6a305119f292fd229cefa257", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryBand" : "100k-150k", "hasSalary" : false, "allSkills" : [ "Community management", "Ecosystem dynamics", "Network effects", "Engagement metrics", "Retention metrics", "Ecosystem health metrics", "Ecosystem leadership", "Relationship strategies", "Feedback loops", "Network-based roles", "Multi-sided platforms", "Relationship management", "Strategic expansion", "Systems-level thinking", "Marketplaces", "SaaS ecosystems", "Distributed networks" ], "skillCount" : 17, "jobCategory" : "marketing", "jobSubcategory" : "growth", "seniorityLevel" : "Senior", "dataQualityScore" : 0.8888889, "_score" : 629.25616, "daysOld" : 1, "hasLocation" : true, "hasGeo" : false, "llmIndustry" : "Technology", "llmIndustryConfidence" : 0.95 }, { "id" : "69ec72e84c40e2acd1d0b696", "title" : "Auditor", "company" : "Texas Workforce Commission", "industry" : "Government", "salary" : { "min" : 48000.0, "max" : 62000.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline data for an entry-level Auditor in Austin, Texas, adjusted for government industry and full-time employment." }, "location" : "826917", "locations" : [ "826917", "
Austin:101 E 15th St (320-4001) (320TX-4001)
101 E 15th St
Austin, 78778-0001

" ], "certifications" : [ "Certified Public Accountant (CPA)", "Certified Internal Auditor (CIA)", "Certified Information Systems Auditor (CISA)", "Certified Texas Contract Manager" ], "timestamp" : "2026-06-16T02:38:39.455", "fingerprint" : "5147c3c4a9845c2b7a15e95ccd25ace0ea8bd4a11b5ebb58196a99451aca906a", "source" : "taleo", "language" : "en", "applyUrl" : "https://aa270.taleo.net/careersection/ex/jobdetail.ftl?job=34602", "externalId" : "34602", "industryFunction" : { }, "structuredLocations" : [ { "city" : "Austin", "state" : "Texas", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "30.2711286,-97.7436995" } ], "locationText" : "Austin, Texas, United States", "minimumEducation" : "Bachelor's", "experienceMinYears" : 0, "experienceMaxYears" : 2, "experienceLevel" : "Entry", "professionalSkills" : [ "Contract monitoring", "Compliance monitoring", "Quality control", "Project management", "Process improvement", "Internal controls" ], "hardSkillsList" : [ "Auditing", "Financial performance", "IT auditing", "Business process analysis", "Agency budgetary controls", "Administrative procedures", "State regulatory laws", "Federal regulatory laws", "Audit reports", "Audit information", "Document evaluation" ], "hardSkills" : [ { "name" : "Auditing", "level" : "intermediate" }, { "name" : "Financial performance", "level" : "intermediate" }, { "name" : "IT auditing", "level" : "intermediate" }, { "name" : "Business process analysis", "level" : "intermediate" }, { "name" : "Agency budgetary controls", "level" : "intermediate" }, { "name" : "Administrative procedures", "level" : "intermediate" }, { "name" : "State regulatory laws", "level" : "intermediate" }, { "name" : "Federal regulatory laws", "level" : "intermediate" }, { "name" : "Audit reports", "level" : "intermediate" }, { "name" : "Audit information", "level" : "intermediate" }, { "name" : "Document evaluation", "level" : "intermediate" } ], "majorSkills" : "Auditing, Contract monitoring, Compliance monitoring", "majorSkillsList" : [ "Auditing", "Contract monitoring", "Compliance monitoring" ], "coreResponsibilities" : "Plan monitoring reviews, Participate in monitoring reviews, Gather monitoring data, Track monitoring data, Assess monitoring data, File monitoring data, Utilize agency systems, Obtain contractor information, Obtain case information, Complete data entry, Develop draft reports, Develop draft correspondence", "domainKnowledge" : "Agency budgetary controls; Administrative procedures; State regulatory laws; Federal regulatory laws", "domainList" : [ "agency-budgetary-controls", "administrative-procedures", "state-regulatory-laws", "federal-regulatory-laws" ], "domainSpecificKnowledge" : [ "Agency budgetary controls", "Administrative procedures", "State regulatory laws", "Federal regulatory laws" ], "communicationSkills" : "Composing reports; Composing correspondence", "documentationSkills" : "Audit reports", "continuousLearning" : "Training opportunities; Development opportunities", "problemSolvingSkills" : "Analytical skills; Identifying areas of change; Making recommendations", "outcomesObjectives" : "Improve processes; Improve internal controls", "qualificationsBasic" : "Graduation from accredited college or university, Four years of full-time experience, License to practice as CPA in Texas, Certificate to practice as CIA, Certificate to practice as CISA", "qualificationsPreferred" : "Certified Texas Contract Manager", "remoteFriendly" : false, "workSiteFlexibility" : "Onsite", "rawDescription" : "WHO WE ARE: Texas Workforce Commission connects people with careers across the state. While we are based in Austin on the north lawn of the Texas State Capitol, we have offices statewide. We're a Family Friendly Certified Workplace with great work-life balance, competitive salaries, extensive opportunities for training and development, and fantastic benefits. This position is located at 101 E. 15th St, Austin, Texas, in the Contract Oversight and Support Department. TWC is not considering applications from individuals who require sponsorship for an employment visa, including those with H1B visas or those currently on student or postgraduate visas. You must be a Texas resident to work for the Texas Workforce Commission or willing to relocate to Texas. WHO YOU ARE: A self-motivated individual who possesses strong analytical skills, attention to detail, and excellent communication skills. A candidate well versed in auditing and monitoring practices, possessing the ability to build and maintain relationships with team members, and a commitment to upholding strong professional standard and ethics. WHAT YOU WILL DO: The Auditor I performs entry-level to routine (journey-level) auditing work. Works under moderate supervision, with limited latitude for the use of initiative and independent judgment. YOU WILL BE TRUSTED TO: -Plan for and participates in assigned monitoring reviews. -Gather, track, assess, and file monitoring-related data and information. -Utilize Agency systems to obtain relevant contractor and case information and complete monitoring-related data entry. -Develop draft monitoring reports and correspondence under the direction of a lead monitor. -Perform other duties as assigned. YOU QUALIFY WITH: - Graduation from an accredited four-year college or university with major course work in accounting, business administration, finance, economics or a related field -OR- -Four years of full-time experience in accounting, program financial performance or IT auditing, or in management information systems analysis, business process analysis, contract monitoring, or in positions which provided extensive knowledge of agency budgetary controls, or administrative procedures and related state and federal regulatory laws may substitute for education. -A license to practice as a Certified Public Accountant (CPA) in Texas will satisfy the minimum requirements. -A certificate to practice as a Certified Internal Auditor (CIA) or as a Certified Information Systems Auditor (CISA) will satisfy the minimum requirements. YOU ARE A GREAT FIT WITH: -Excellent communication skills -Experience in auditing, contract monitoring, or compliance monitoring. -Quality control or project management experience. -Proficiency in composing clear and concise audit reports, gathering and analyzing accurate and relevant audit information, and evaluating documents for accuracy and legal conformance. -Experience with identifying areas of needed change and making recommendations to improve processes and internal controls. -Certified Texas Contract Manager to assist with Contract Oversight and Support department’s work and mission. YOU GAIN: - A Family Friendly Certified Workplace. - Competitive salary: $4,650.00/month - Defined Retirement Benefit Plan - Optional 401(k) and 457 accounts - Medical Insurance - Paid time off, including time for vacation, sick and family care leave - Additional benefits for active employees can be found at https://www.ers.texas.gov/Active-Employees/Health-Benefits. VETERANS: Use your military skills to qualify for this position or other jobs! Go to www.texasskillstowork.com to translate your military work experience and training courses into civilian job terms, qualifications, and skill sets. Also, you can compare this position to military occupations (MOS) at the Texas State Auditor's Office by pasting this link into your browser: https://hr.sao.texas.gov/Compensation/MilitaryCrosswalk/MOSC_AccountingAuditingandFinance.pdf HOW TO APPLY: To be considered, please complete a State of Texas Application for Employment and apply online at www.workintexas.com or on Taleo ( Job Search ). TWC is not considering applications from individuals who require sponsorship for an employment visa, including those with H1B visas or those currently on student or postgraduate visas In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification document form upon hire. A position utilizing this classification will be designated as security sensitive according to the Texas Labor Code, Section 301.042. United States-Texas-Austin", "summaryDescription" : "Auditor at Texas Workforce Commission. Skills: Auditing, Contract monitoring, Compliance monitoring. Plan monitoring reviews. Participate in monitoring reviews", "createdAt" : "2026-06-14T18:48:01.510", "lastUpdated" : "2026-06-16T02:38:39.455", "ingestTimestamp" : "2026-06-14T18:48:01.510", "mongoId" : "69ec72e84c40e2acd1d0b696", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 48000, "salaryAnnualMax" : 62000, "salaryBand" : "30k-50k", "hasSalary" : true, "allSkills" : [ "Auditing", "Financial performance", "IT auditing", "Business process analysis", "Agency budgetary controls", "Administrative procedures", "State regulatory laws", "Federal regulatory laws", "Audit reports", "Audit information", "Document evaluation", "Contract monitoring", "Compliance monitoring", "Quality control", "Project management", "Process improvement", "Internal controls", "Certified Public Accountant (CPA)", "Certified Internal Auditor (CIA)", "Certified Information Systems Auditor (CISA)", "Certified Texas Contract Manager" ], "skillCount" : 21, "jobCategory" : "finance", "jobSubcategory" : "accounting", "seniorityLevel" : "Entry", "dataQualityScore" : 0.7777778, "_score" : 622.9604, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Government", "llmIndustryConfidence" : 0.95 }, { "id" : "6a305640f292fd229cf264ab", "title" : "AI-Native Auditor", "company" : "Modus", "industry" : "Audit", "salary" : { "min" : 75000.0, "max" : 95000.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : true, "estimation_basis" : "Market baseline for an Entry-level AI-Native Auditor in New York, Financial Services, adjusted for company size and industry." }, "location" : "New York", "locations" : [ "New York" ], "certifications" : [ "CPA", "CPA candidate" ], "timestamp" : "2026-06-16T08:06:20.472", "fingerprint" : "d41e4433af9213d7746d0950fc6f2b91c0123fd0a3086ceb80cee9180f1884a6", "source" : "ashby", "language" : "en", "applyUrl" : "https://jobs.ashbyhq.com/modus/1d21500c-0aa1-4eac-aebf-fa444cbac88e/application", "externalId" : "1d21500c-0aa1-4eac-aebf-fa444cbac88e", "industryFunction" : { }, "structuredLocations" : [ { "city" : "New York", "state" : "New York", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "40.7127281,-74.0060152" } ], "locationText" : "New York, New York, United States", "employmentType" : "FULL_TIME", "experienceMinYears" : 0, "experienceMaxYears" : 4, "experienceLevel" : "Entry", "softSkillsList" : [ "Comfortable with ambiguity" ], "softSkills" : [ { "name" : "Comfortable with ambiguity" } ], "coreResponsibilities" : "Complete audit workpapers end-to-end, Pull ready work from system, Own quality of everything shipped, Partner with reviewers, Push the product frontier, Provide feedback on tools", "teamCollaboration" : "Partnering with reviewers; Partnering with partners", "toolProficiency" : [ "Modus platform" ], "qualificationsBasic" : "0–4 years experience, CPA or CPA candidate, On track for CPA", "remoteFriendly" : true, "rawDescription" : "We're hiring AI-Native Auditors Early-career and already living in AI tools? We'll hand you our platform and let you out-produce an entire team. What You'll Do - Complete audit workpapers end-to-end, paired with our AI platform, at a speed and volume no traditional team can match - Pull ready work from Modus' practice management system tp keep engagements moving without waiting to be told what's next - Own the quality of everything you ship, partnering with reviewers and partners through sign-off - Push the product frontier — you'll be one of our heaviest users, and your feedback directly shapes the tools every Modus auditor relies on What We're Looking For - Early-career auditor (roughly 0–4 years); CPA, CPA candidate, or on track - Already living in AI tools and obsessed with using them to work faster - Comfortable with ambiguity - High-motor Why Modus Modus partners with firms by deploying purpose-built AI and growth resources to make our professionals' lives easier. We're building the future of accounting, and are looking to hire ambitious talent to help get there. ", "summaryDescription" : "AI-Native Auditor at Modus. Complete audit workpapers end-to-end. Pull ready work from system", "createdAt" : "2026-06-16T00:30:32.709", "lastUpdated" : "2026-06-16T08:06:20.472", "ingestTimestamp" : "2026-06-16T00:30:32.709", "mongoId" : "6a305640f292fd229cf264ab", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "salaryAnnualMin" : 75000, "salaryAnnualMax" : 95000, "salaryBand" : "75k-100k", "hasSalary" : true, "allSkills" : [ "Comfortable with ambiguity", "CPA", "CPA candidate", "Modus platform" ], "skillCount" : 4, "jobCategory" : "finance", "jobSubcategory" : "accounting", "seniorityLevel" : "Entry", "dataQualityScore" : 0.8888889, "_score" : 580.96246, "hasLocation" : true, "hasGeo" : true, "llmIndustry" : "Financial Services", "llmIndustryConfidence" : 0.95 }, { "id" : "6a2b0db574d8ee00a0760811", "title" : "AI Trainer - Freelance Annotator (English)", "company" : "Toloka Annotators", "salary" : { "max" : 18.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "Reading, Pennsylvania, United States", "locations" : [ "Reading, Pennsylvania, United States" ], "timestamp" : "2026-06-14T18:52:02.297", "fingerprint" : "99bcc7c08424db01812e14e26d86149f9313c087a6297fcb60ecbddf519bd95d", "source" : "workable", "language" : "en", "applyUrl" : "https://apply.workable.com/j/C4C5E967F9/apply", "externalId" : "C4C5E967F9", "locationPoint" : "39.22367,-84.44216", "structuredLocations" : [ { "city" : "Reading", "state" : "Pennsylvania", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "40.3353450,-75.9279495" } ], "locationText" : "Reading, Pennsylvania, United States", "employmentType" : "PART_TIME", "minimumEducation" : "Bachelor's", "coreResponsibilities" : "Evaluate AI-generated responses, Provide feedback, Follow quality standards, Collaborate with QA Specialists", "domainKnowledge" : "AI systems; Online shopping", "domainList" : [ "ai-systems", "online-shopping" ], "domainSpecificKnowledge" : [ "AI systems", "Online shopping" ], "specialRequirements" : "Written English proficiency", "qualificationsBasic" : "Bachelor's degree, 1 year experience", "qualificationsPreferred" : "Written English C1/C2, Stable internet connection", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "_Please submit your CV in English and indicate your level of English proficiency. _ Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems.** Participation is project-based, not permanent employment.** **What this opportunity involves** As an AI Trainer - Annotator, your contribution will help train AI models, shaping how they understand and interact with the world. This isn't just traditional feedback - you'll be evaluating and improving how AI reasons, responds, and handles real-world tasks like online shopping experiences. **While each project involves unique tasks, contributors may:** * Evaluating AI-generated responses across a range of tasks, including e-commerce and online shopping scenarios, drawing on your own experience as an online shopper. * Providing honest, detailed, and useful feedback that helps improve model and agent performance. * Following style and quality standards to ensure consistency. * Collaborating with Quality Assurance Specialists to refine and improve content. **What we look for** If you’re a professional who works with text, Mindrift offers a unique opportunity to apply your writing, editing, and creative and communication skills to an AI training project. This opportunity is a good fit if you are seeking for open to part-time, non-permanent projects. Ideally, contributors will have: * Bachelor’s degree in any subject field; * At least 1 year of professional or educational experience in any field; * Strong written English (C1/C2); * Stable internet connection. **How it works** Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid **Project time expectations ** For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. **Compensation ** On this project, contributors can earn up to **$18 per hour equivalent** , depending on their level and pace of contribution. Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.", "summaryDescription" : "AI Trainer - Freelance Annotator (English) at Toloka Annotators. Evaluate AI-generated responses. Provide feedback", "postedDate" : "2026-06-11T00:00:00.000", "createdAt" : "2026-06-12T06:15:56.354", "lastUpdated" : "2026-06-14T18:52:02.297", "ingestTimestamp" : "2026-06-12T06:15:56.354", "mongoId" : "6a2b0db574d8ee00a0760811", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "sourceUrl" : "https://apply.workable.com/j/C4C5E967F9", "salaryAnnualMax" : 37440, "hasSalary" : false, "allSkills" : [ "AI systems", "Online shopping" ], "skillCount" : 2, "seniorityLevel" : "mid", "dataQualityScore" : 0.7777778, "_score" : 563.99475, "daysOld" : 3, "hasLocation" : true, "hasGeo" : true }, { "id" : "6a2b0db574d8ee00a076080f", "title" : "AI Trainer - Freelance Annotator (English)", "company" : "Toloka Annotators", "salary" : { "max" : 18.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "Remote", "locations" : [ "Remote" ], "timestamp" : "2026-06-14T18:33:16.189", "fingerprint" : "1aee247c35981cab49ac682a99cab461038cf5fc210be28ebcc913f8a02e7376", "source" : "workable", "language" : "en", "applyUrl" : "https://apply.workable.com/j/F2C0D1CFA2/apply", "externalId" : "F2C0D1CFA2", "locationPoint" : "39.8283,-98.5795", "structuredLocations" : [ { "workMode" : "remote" } ], "locationText" : "", "employmentType" : "PART_TIME", "minimumEducation" : "Bachelor's", "coreResponsibilities" : "Evaluate AI-generated responses, Provide feedback, Follow style and quality standards, Collaborate with Quality Assurance Specialists", "domainKnowledge" : "AI systems; Online shopping experiences", "domainList" : [ "ai-systems", "online-shopping-experiences" ], "domainSpecificKnowledge" : [ "AI systems", "Online shopping experiences" ], "specialRequirements" : "Submit CV in English", "qualificationsBasic" : "Bachelor's degree, 1 year of experience, Written English (C1/C2), Stable internet connection", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "_Please submit your CV in English and indicate your level of English proficiency. _ Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems.** Participation is project-based, not permanent employment.** **What this opportunity involves** As an AI Trainer - Annotator, your contribution will help train AI models, shaping how they understand and interact with the world. This isn't just traditional feedback - you'll be evaluating and improving how AI reasons, responds, and handles real-world tasks like online shopping experiences. **While each project involves unique tasks, contributors may:** * Evaluating AI-generated responses across a range of tasks, including e-commerce and online shopping scenarios, drawing on your own experience as an online shopper. * Providing honest, detailed, and useful feedback that helps improve model and agent performance. * Following style and quality standards to ensure consistency. * Collaborating with Quality Assurance Specialists to refine and improve content. **What we look for** If you’re a professional who works with text, Mindrift offers a unique opportunity to apply your writing, editing, and creative and communication skills to an AI training project. This opportunity is a good fit if you are seeking for open to part-time, non-permanent projects. Ideally, contributors will have: * Bachelor’s degree in any subject field; * At least 1 year of professional or educational experience in any field; * Strong written English (C1/C2); * Stable internet connection. **How it works** Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid **Project time expectations ** For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. **Compensation ** On this project, contributors can earn up to **$18 per hour equivalent** , depending on their level and pace of contribution. Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.", "summaryDescription" : "AI Trainer - Freelance Annotator (English) at Toloka Annotators. Evaluate AI-generated responses. Provide feedback", "postedDate" : "2026-06-11T00:00:00.000", "createdAt" : "2026-06-12T04:58:29.303", "lastUpdated" : "2026-06-14T18:33:16.189", "ingestTimestamp" : "2026-06-12T04:58:29.303", "mongoId" : "6a2b0db574d8ee00a076080f", "processingStatus" : "llm_updated", "llmProcessingStatus" : true, "sourceUrl" : "https://apply.workable.com/j/F2C0D1CFA2", "salaryAnnualMax" : 37440, "hasSalary" : false, "allSkills" : [ "AI systems", "Online shopping experiences" ], "skillCount" : 2, "seniorityLevel" : "mid", "dataQualityScore" : 0.7777778, "_score" : 563.99475, "daysOld" : 3, "hasLocation" : true, "hasGeo" : false }, { "id" : "6a2b0db574d8ee00a0760800", "title" : "AI Trainer - Freelance Annotator (English)", "company" : "Toloka Annotators", "salary" : { "max" : 18.0, "currency" : "USD", "frequency" : "yearly", "is_estimated" : false }, "location" : "Fort Collins, Colorado, United States", "locations" : [ "Fort Collins, Colorado, United States" ], "timestamp" : "2026-06-14T19:36:11.529", "fingerprint" : "9ae5d268f8c8110f9368ae6281655671f799043f5f58d26a90dfad7848ba221e", "source" : "workable", "language" : "en", "applyUrl" : "https://apply.workable.com/j/3EFC0AE526/apply", "externalId" : "3EFC0AE526", "locationPoint" : "40.58526,-105.08442", "structuredLocations" : [ { "city" : "Fort Collins", "state" : "Colorado", "country" : "United States", "continent" : "na", "source" : "llm", "workMode" : "onsite", "geo" : "40.5871782,-105.0770113" } ], "locationText" : "Fort Collins, Colorado, United States", "employmentType" : "PART_TIME", "minimumEducation" : "Bachelor's", "coreResponsibilities" : "Evaluate AI-generated responses, Provide feedback, Follow style and quality standards, Collaborate with Quality Assurance Specialists", "domainKnowledge" : "AI systems; Online shopping", "domainList" : [ "ai-systems", "online-shopping" ], "domainSpecificKnowledge" : [ "AI systems", "Online shopping" ], "qualificationsBasic" : "Bachelor's degree, 1 year experience, Written English C1/C2, Stable internet connection", "remoteFriendly" : true, "workSiteFlexibility" : "Remote", "rawDescription" : "_Please submit your CV in English and indicate your level of English proficiency. _ Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems.** Participation is project-based, not permanent employment.** **What this opportunity involves** As an AI Trainer - Annotator, your contribution will help train AI models, shaping how they understand and interact with the world. This isn't just traditional feedback - you'll be evaluating and improving how AI reasons, responds, and handles real-world tasks like online shopping experiences. **While each project involves unique tasks, contributors may:** * Evaluating AI-generated responses across a range of tasks, including e-commerce and online shopping scenarios, drawing on your own experience as an online shopper. * Providing honest, detailed, and useful feedback that helps improve model and agent performance. * Following style and quality standards to ensure consistency. * Collaborating with Quality Assurance Specialists to refine and improve content. **What we look for** If you’re a professional who works with text, Mindrift offers a unique opportunity to apply your writing, editing, and creative and communication skills to an AI training project. This opportunity is a good fit if you are seeking for open to part-time, non-permanent projects. Ideally, contributors will have: * Bachelor’s degree in any subject field; * At least 1 year of professional or educational experience in any field; * Strong written English (C1/C2); * Stable internet connection. **How it works** Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid **Project time expectations ** For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. **Compensation ** On this project, contributors can earn up to **$18 per hour equivalent** , depending on their level and pace of contribution. Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.", "summaryDescription" : "AI Trainer - Freelance Annotator (English) at Toloka Annotators. Evaluate AI-generated responses. 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This isn't just traditional feedback - you'll be evaluating and improving how AI reasons, responds, and handles real-world tasks like online shopping experiences. **While each project involves unique tasks, contributors may:** * Evaluating AI-generated responses across a range of tasks, including e-commerce and online shopping scenarios, drawing on your own experience as an online shopper. * Providing honest, detailed, and useful feedback that helps improve model and agent performance. * Following style and quality standards to ensure consistency. * Collaborating with Quality Assurance Specialists to refine and improve content. **What we look for** If you’re a professional who works with text, Mindrift offers a unique opportunity to apply your writing, editing, and creative and communication skills to an AI training project. This opportunity is a good fit if you are seeking for open to part-time, non-permanent projects. 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