Stack AV
Tech / AI / Software
SeniorSoftwareEngineer,BackendInfrastructure(Labeling)
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“Senior Software Engineer, Backend Infrastructure (Labeling) at Stack AV. Skills: Backend Infrastructure, Labeling, Scalable Systems, Data Pipelines, ML Integration, API Development, Infrastructure & DevOps, Python, SQL. design, build, and scale the mission-critical systems that power our data flywheel. responsible for the high-performance architecture that manages massive datasets, orchestrates complex human-in-the-loop workflows, and integrates seamlessly with our machine learning pipelines”
What You'll Achieve.
high-performance architecture that manages massive datasets; orchestrates complex human-in-the-loop workflows; integrates seamlessly with our machine learning pipelines; labeling operations are fast, reliable, and capable of handling the next order of magnitude in data volume; Design and maintain robust, distributed backend services capable of managing millions of labeling tasks and high-throughput data streams with minimal latency; deliver high-quality labeled outputs to model training environments; maximize human efficiency; ensure 99. 9+% availability, focusing on observability, automated scaling, and cost-efficiency; track the lifecycle of every log from unlabeled to production-ready; Build scalable backend services for automated QA to and AI-assisted labeling plugins
Industry & Context.
Stack AV complies with all applicable U. S. national security laws, regulations, and administrative requirements, which can restrict Stack AV’s ability to employ certain persons in certain positions pursuant to a range of national security-related requirements., This position may be contingent upon Stack AV verifying a candidate’s residence, U. S. person status, and/or citizenship status., This position may also involve working with software and technologies subject to U. S. export control regulations., Under these regulations, it may be necessary for Stack AV to obtain a U. S. government export license prior to releasing its technologies to certain persons., If Stack AV determines that a candidate’s residence, U. S. person status, and/or citizenship status will require a license, prohibit the candidate from working in this position, or otherwise be subject to national security-related restrictions, Stack AV expressly reserves the right to either consider the candidate for a different position that is not subject to such restrictions, on whatever terms and conditions Stack AV shall establish in its sole discretion, or, in the alternative, decline to move forward with the candidate’s application.
What They're Looking For.
Must Have
Proven track record of building scalable, reliable infrastructure in a fast-paced environment., Ability to collaborate effectively across teams., development experience with Python and SQL.
Nice to Have
Prior experience with Trino, Flyte / Airflow, and Kubernetes are a plus., Prior experience with ML Ops workflows is a plus., Prior experience building and managing data platforms for multimodal ML needs is a plus., Prior experience with agentic workflows is a plus., Prior experience in autonomous vehicles (AV) is a plus.
What You'll Do.
and scale the mission-critical systems that power our data flywheel
responsible for the high-performance architecture that manages massive datasets
orchestrates complex human-in-the-loop workflows
and integrates seamlessly with our machine learning pipelines
ensures that our labeling operations are fast
and capable of handling the next order of magnitude in data volume
Design and maintain robust
distributed backend services capable of managing millions of labeling tasks and high-throughput data streams with minimal latency
Build and refine processes to ingest raw data from diverse sources and deliver high-quality labeled outputs to model training environments
Implement "Active Learning" and "Model-in-the-loop" features
enabling automated pre-labeling and intelligent task prioritization to maximize human efficiency
Develop and document clean
performant APIs that serve as the bridge between our front-end labeling tools
and internal ML platforms
Manage cloud-native infrastructure to ensure 99. 9+% availability
focusing on observability
Play a key role in designing and building the next generation of Stack’s Labeling Infrastructure
Implement robust architecture to track the lifecycle of every log from unlabeled to production-ready
Build scalable backend services for automated QA to and AI-assisted labeling plugins
Write high quality Python and SQL
How You'll Work.
Team & Collaboration
Partner with AI team and Product Managers to translate complex data requirements into technical specifications and durable backend solutions.; Ability to collaborate effectively across teams.
Full Job Description
About Stack: Stack is developing revolutionary AI and advanced autonomous systems designed to enhance safety, reliability, and efficiency of modern operations. Stack's autonomous technology incorporates cutting-edge advancements in artificial intelligence, robotics, machine learning, and cloud technologies, empowering us to create innovative solutions that address the needs and challenges of the dynamic trucking transportation industry. With decades of experience creating and deploying real world systems for demanding environments, the Stack team is dedicated to developing an autonomous solution ecosystem tailored to the trucking industry's unique demands. About the Role: As a Labeling Infrastructure Backend Engineer, you will design, build, and scale the mission-critical systems that power our data flywheel. You will be responsible for the high-performance architecture that manages massive datasets, orchestrates complex human-in-the-loop workflows, and integrates seamlessly with our machine learning pipelines. Your work ensures that our labeling operations are fast, reliable, and capable of handling the next order of magnitude in data volume. Architect Scalable Systems: Design and maintain robust, distributed backend services capable of managing millions of labeling tasks and high-throughput data streams with minimal latency. Optimize Data Pipelines: Build and refine processes to ingest raw data from diverse sources and deliver high-quality labeled outputs to model training environments. ML Integration: Implement "Active Learning" and "Model-in-the-loop" features, enabling automated pre-labeling and intelligent task prioritization to maximize human efficiency. API Development: Develop and document clean, performant APIs that serve as the bridge between our front-end labeling tools, third-party vendors, and internal ML platforms. Infrastructure & DevOps: Manage cloud-native infrastructure to ensure 99.9+% availability, focusing on observability, automated scaling, a
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