ML Validation Engineer — Autonomy & Simulation (Hybrid)

General Motors; Sunnyvale (CA); Hybrid; USD 120,000 - 160,000; Full time

Job summary

General Motors seeks an ML Validation Research Engineer (Early Career) to advance verification and validation of ML components in robotics and autonomous driving. The role focuses on simulation-based evaluation, uncertainty modeling, scenario generation, and translating ML research into production-ready tools within CI/CD pipelines.

Responsibilities include developing AI-centric automation for issue triage, improving observability, and contributing to validation methodology with cross-functional

Qualifications

• B.Sc or MS in ML, robotics, CS or related field.
• Strong Python and ML framework experience.
• Ability to translate ML research ideas into functional prototypes.
• Experience integrating ML evaluation into CI/CD pipelines.

Responsibilities

• Prototype research concepts into performant tools integrated into CI/CD pipelines.
• Develop AI tools to improve performance monitoring and observability for autonomous vehicle stack.
• Advance ML research for simulation validation and coverage.
• Develop AI-centric automations to scale issue triage and root cause analysis.
• Create scenario generation and rare-event discovery tooling.
• Develop metrics, predictors, uncertainty and OOD detection methods for autonomy ML systems.
• Evaluate deep learning modules across perception, prediction, and planning in realistic simulation.
• Collaborate with cross-functional partners and author technical docs.

Skills

Python

PyTorch

JAX

TensorFlow

CI/CD pipelines

Education

B.Sc or MS in ML, Robotics, CS

Tools

CI/CD pipelines

Job description

General Motors seeks an ML Validation Research Engineer (Early Career) to advance verification and validation of ML components in robotics and autonomous driving. The role focuses on simulation-based evaluation, uncertainty modeling, scenario generation, and translating ML research into production-ready tools within CI/CD pipelines.

Responsibilities include developing AI-centric automation for issue triage, improving observability, and contributing to validation methodology with cross-functional