Staff ML Systems Engineer
Job Summary
The Data Labeling Engineering team at General Motors is seeking a Staff ML Systems Engineer to design, build, and operate hybrid human/machine data labeling tools and pipelines. This role involves defining platform vision, owning projects end-to-end, and collaborating across the AV stack to power autonomous vehicle machine learning models. The ideal candidate will have extensive experience building robust distributed platforms and leveraging modern full-stack technologies and AI tools.
Responsibilities
- Define platform vision and roadmap.
- Own technical projects end-to-end.
- Collaborate across the AV stack.
- Apply ML to labeling data workflows.
Required Skills
- 8+ years in distributed platforms.
- Proficiency in Python, TypeScript, Go, React.
- Experience with AI tools and workflows.
- Strong system design and algorithms.
Job Details
What You'll Do
- Define the platform vision and roadmap
- Own projects end-to-end
- Collaborate across the AV stack
- Level up how ML teams work with data
- Apply ML to labeling itself
- Build high-impact labeling experiences
- Champion AI-assisted engineering
Your Skills & Abilities
- Passionate about self-driving/robotics technology
- Proven experience shipping and operating end-to-end products or features in production
- Strong communication and collaboration skills
- Driven to learn new technologies and deepen expertise across frontend, backend, and data/ML-adjacent systems
- Empathetic to user challenges
Requirements
- 8+ years of experience building robust distributed platforms and applications
- Hands-on experience leveraging AI tools to accelerate understanding, implementation, debugging, and delivery
- Proficiency in writing and reviewing high-quality, scalable, and performant full-stack code using 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, data structures, algorithms, and engineering best practices