Amazon.com Services LLC
Software Development, Language Engineer, Retail
LanguageEngineer,CoreSearch
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Language Engineer, Core Search at Amazon.com Services LLC. Skills: Language Engineering, Data Annotation, Natural Language Processing. Design data annotation guidelines. Develop data annotation guidelines”
What You'll Achieve.
Deliver on stakeholder requirements; Improve customer experience; Measure search quality; Produce metrics for AI models; Produce metrics for customer experience
Industry & Context.
Problem solving
What They're Looking For.
Must Have
2+ years computational linguistics experience, 2+ years language data processing experience, 2+ years semantics experience, 2+ years philosophy of language experience, Master's degree in Linguistics, 5+ years relevant professional experience, Proficiency in Python, Knowledge of Regex, Knowledge of SQL, Knowledge of MS Excel, Knowledge of Git, Navigate Unix terminal, Use common command line tools, Familiarity with annotation tools, Familiarity with annotation workflows, Excellent communication skills, Excellent organizational skills, Keen eye for details, Comfortable in fast-paced environment, Comfortable in collaborative environment, Comfortable in dynamic work environment, Willingness to support several projects, Accept reprioritization as necessary
Nice to Have
Proficient in French, Proficient in German, Proficient in Dutch, Proficient in Italian, Proficient in Spanish, Proficient in Japanese, Experience in data science, Experience in quantitative research, Experience with language annotation, Experience with data markup, Hands-on ML experience, Hands-on DL experience, NLP experience, Search experience, Experience with AWS services, Knowledge of user experience concepts, Knowledge of user experience methods, Familiarity with online retail, Familiarity with e-commerce
What You'll Do.
Design data annotation guidelines
Develop data annotation guidelines
Design data annotation workflows
Develop data annotation workflows
Manage large amounts of data
Process large amounts of data
Adopt quality control metrics
Design quality control metrics
Evaluate data annotation quality
Maximize productivity
Maximize process efficiency
Standardize processes
Conduct investigations
Handle annotation requests
Handle data investigation requests
Deliver based on priorities
Be flexible in changes
Change workflows accordingly
Write annotation guidelines
Support data wrangling
Support data analysis
Define UI template specifications
Report data quality metrics
Train junior data associates
Onboard junior data associates
How You'll Work.
Team & Collaboration
Cross-functional teams; Global product teams; Global design teams; Global science teams; Global operations teams; Global engineering teams; Scientists; Engineers; Product managers
Communication Scope
Excellent communication
Full Job Description
We are the MIDAS team within the Amazon Search organization. We own the end-to-end human annotation requirements for our product partners. This is an opportunity to join a high-performing team that helps customers worldwide find and discover the best products, meet their personalized needs with the right product details, and receive comparisons, recommendations, and more on Amazon. This role is high-visibility and highly cross-functional, requiring collaboration and influence across global product, design, science, operations, and engineering teams. We are looking for a Language Engineer to join MIDAS to write intuitive, labeler-friendly annotation guidelines that enable measurement of search quality, support data wrangling and analysis, define specifications for labeling UI templates, and report labeled-data quality metrics to deliver on stakeholder requirements and improve the customer experience. To achieve high accuracy and consistency in labeled data outputs, Language Engineers apply linguistic (e.g., semantics, syntax, pragmatics) and scripting expertise to solve natural language processing and language understanding challenges. Key job responsibilities Design and develop data annotation guidelines and workflows. Manage and process large amounts of structured and unstructured data. Adopt and design quality control metrics and methodology to evaluate the quality of data annotation. Maximize productivity, process efficiency and quality through streamlined workflows, process standardization, documentation, audits and investigations on a periodic basis. Handle annotation & data investigation requests from multiple stakeholders with high efficiency and quality in a fast-paced environment. Collaborate with scientists, engineers, and product managers in defining metrics, guidelines, and workflows. Initiate and contribute towards improvement projects, present solution proposals, and implement them. Establish processes and mechanisms to onboard and train junior data as
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