Staff ML Systems Engineer
About The Position
The Data Labeling Engineering team at General Motors designs, builds, and operates hybrid human- and machine-powered data labeling tools and pipelines that supply training data for autonomous vehicle machine-learning models. The team works across software engineering, data engineering, and AI/ML to develop strategies, tools, and quality controls for reliable training data at scale. Its platform serves thousands of users and consumers and uses a full-stack architecture that includes TypeScript, React, Python, GraphQL, Go, and ML model services. The Staff ML Systems Engineer will help shape the platform vision and roadmap, own technical projects from problem framing through rollout, and collaborate with teams across machine learning, operations, product, data science, and platform engineering. The role focuses on turning partner needs into workflows, APIs, and user interfaces that meet quality, cost, and latency goals; improving data and labeling workflows through automation and tooling; and integrating machine-learning-driven annotation approaches such as pre-labeling, autolabeling, and active learning. The engineer will build and test scalable, high-performance services and user experiences, and champion AI-assisted development workflows while maintaining code and product quality. The position is hybrid, with the selected candidate expected to report to a specific location at least three times per week or at another frequency set by their manager. Travel is required for less than 25% of the role. Relocation benefits are available to candidates who qualify under company policy.
Requirements
- At least 8 years of experience building robust distributed platforms and applications.
- Hands-on experience using AI tools and workflows, such as agentic workflows, knowledge acquisition, documentation generation, or operational triage, to accelerate understanding, implementation, debugging, and delivery.
- Proficiency writing and reviewing high-quality, scalable, performant full-stack code using technologies such as Python, TypeScript, Go, React, SQL, Redux, gRPC, GraphQL, and WebGL.
- Solid understanding of scalable software system design, including data modeling and API/interface design.
- Strong fundamentals in object-oriented design, design patterns, data structures, algorithms, and engineering best practices such as TDD, code quality, observability, and CI/CD.
- Proven experience shipping and operating end-to-end products or features in production.
- Strong communication and collaboration skills, including the ability to explain tradeoffs, influence peers, and work through ambiguity with cross-functional partners.
- Willingness to learn new technologies and deepen expertise across frontend, backend, and data/ML-adjacent systems.
- Empathy for the challenges of labelers, ML engineers, and Operations, and an interest in turning complex workflows into intuitive tools.
- Passion for self-driving and robotics technology and its potential to transform safety, mobility, and the human experience.
Nice To Haves
- A track record of close collaboration with customers, product managers, designers, and/or user experience researchers.
- Experience with computer vision, machine learning, or data-centric AI projects, especially those involving data annotation, data quality, or autolabeling loops.
- Familiarity with data labeling or annotation platforms and tools used by large labeling workforces, such as annotation user interfaces, workflow engines, and quality systems.
- Experience with A/B testing and telemetry or observability systems to measure impact and reliability.
- Experience developing data-intensive or visualization-heavy applications.
Responsibilities
- Define the platform vision and roadmap by gathering cross-team and cross-functional needs and translating them into an aligned, incremental development plan.
- Own technical projects end-to-end, from problem framing through design, implementation, and rollout.
- Drive code reviews, design discussions, and technical decisions.
- Collaborate with ML Engineering, Operations, Product, Data Science, and other platform teams to turn requirements into workflows, APIs, and user interfaces that meet quality, cost, and latency goals.
- Develop automation and tooling that provide insight into labeling workflows and data quality, such as efficiency dashboards, automated quality assurance, and autolabel review tools.
- Collaborate with ML engineers to design and integrate machine-learning-driven annotation, including pre-labeling, autolabeling, and active-learning loops.
- Design, implement, and test scalable, high-performance user experiences and services using modern full-stack and/or frontend technologies.
- Use and advocate for AI-powered development workflows, such as code assistants, automated documentation, and test generation, to increase build velocity while maintaining quality.
Benefits
- Medical, dental, and vision benefit options.
- Health Savings Account and Flexible Spending Accounts.
- Retirement savings plan.
- Sickness and accident benefits.
- Life insurance.
- Paid vacation and holidays.
- Tuition assistance programs.
- Employee assistance program.
- GM vehicle discounts.
- Incentive pay program with potential payouts based on company performance, job level, and individual performance.
- Relocation benefits for candidates who qualify under company policy.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
