Help teach our self-driving vehicles how to see and understand the world.
The Data Labeling Engineering team designs, builds, and operates hybrid human/machine data labeling tools and pipelines that power autonomous vehicle machine learning models within General Motors' AV organization . We operate in the intersection of software engineering , data engineering , and AI/ML , defining the strategies, tooling, and quality controls that create reliable training data at scale. Our tools and platform are used by thousands of users and consumers.
We own a modern full‑stack architecture including TypeScript/React, Python, GraphQL, Golang , and ML model services , which powers data‑annotation pipelines and machine‑led training data solutions at foundation ‑ model scale . We partner closely across AI/ML engineers , Product Operations , Product Management , Data Science , and other ML
Platform groups.
As an early-career Software Engineer on the Data Labeling Engineering team, you will build tools and services that help machine learning teams create high-quality training data for autonomous driving. Your work may span frontend experiences, backend services, data pipelines, machine learning integrations, and quality systems used by labelers, ML engineers, and operations teams.
This role is designed for a recent college graduate or engineer early in their career who wants to own meaningful pieces of a platform, grow their technical expertise, and work directly on systems that enable the next generation of AV capabilities. You will learn from experienced engineers while contributing to production systems and developing depth across frontend, backend, data, and ML-adjacent technologies.
Level up how ML teams work with data
Develop automation and tooling that give ML engineers deep insight into labeling workflows and data quality (e.g., efficiency dashboards, auto‑QA, autolabel review tools), reducing iteration time from idea to trained model.
Apply ML to labeling itself
Collaborate with ML engineers to design and integrate ML‑driven data annotation (pre‑labeling, autolabeling, active learning loops), helping us move from human‑only to machine‑led labeling at scale.
Build high ‑ impact labeling experiences
Design, implement, and test scalable, high‑performance user experiences and services using modern full‑stack and/or frontend technologies. You’ll ship features spanning multiple surface-areas that directly affect how quickly and accurately we can label data for new models and cities.
Champion AI ‑ assisted engineering
Use and advocate for modern AI‑powered development workflows (code assistants, automated documentation, test generation, etc.) to increase build-velocity while maintaining code and product quality.
Recently completed a bachelor’s, master’s, or PhD degree in Computer Science, Computer Engineering, Software Engineering, Artificial Intelligence, Machine Learning, or a related STEM field. For completed degrees, graduation must have occurred within the past 12 months.
Experience shipping software or features through internships, research, academic projects, or prior professional work.
Programming experience in one or more languages such as Python, TypeScript, JavaScript, Go, Java, or C++.
Familiarity with software fundamentals, including o bject-oriented design, design patterns, data structures, algorithms, API/interface design , and engineering best practices.
Strong communication and collaboration skills; you can explain tradeoffs, influence peers, and work through ambiguity with cross‑functional partners.
Interest in autonomous vehicles, robotics, machine learning, data-centric AI, or developer and ML platform technologies.
Degree completed between May 2025 and August 2026, with availability to begin employment in 2026.
Hands-on experience leveraging AI tools (agentic workflows, knowledge acquisition, documentation generation, operational triage, etc) to accelerate understanding, implementation, debugging, and delivery of new capabilities.
Proficiency in writing and reviewing high‑quality, scalable, and performant full-stack code using technologies and languages like Python, TypeScript, Go, React, SQL, Redux, gRPC, GraphQL, WebGL, etc .
Solid understanding of scalable software system design including data modeling and API/interface design.
Strong fundamentals in object ‑ oriented design and design patterns , data structures , algorithms , and engineering best practices (TDD, code quality, observability, CI/CD).
Driven to learn new technologies and deepen your expertise across frontend, backend, and data/ML‑adjacent systems.
Empathetic to user challenges (from labelers to ML engineers to Ops) and excited to turn messy workflows into simple, intuitive tools .
Hybrid: This role is categorized as hybrid. This means the successful candidate is expected to report to our Suynnvale , CA office three times per week, at minimum.
This job may be eligible for relocation benefits
Compensation: The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of New York, Colorado, California, or Washington.
The salary range for this role is $125,000 to $165,000. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.
Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.
Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.
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We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire.
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