Staff Onboard ML Engineer - Embodied AI for Autonomy

General Motors; Mountain View (CA); Hybrid; USD 180,000 - 280,000; Full time

Benefits offered by this job

Company vehicle program

Relocation benefits

Job summary

General Motors is seeking a senior ML engineer to design, develop, and deploy onboard ML models for autonomous driving. You will lead end-to-end projects from concept to onboard implementation, focusing on real-time inference, robustness, and safety-critical operation.

You will advance neural networks, training, and optimization techniques, mentor teams, and collaborate across engineering groups to align ML roadmaps with vehicle product milestones.

Qualifications

• Master's or Ph.D. in Machine Learning, Robotics, Computer Science, Electrical Engineering, or related field.
• 4+ years of experience with large-scale foundation models (LLMs, VLAs, vision models).
• Experience deploying real-time onboard ML systems in safety-critical contexts.
• Proven track record building robust DL models mapping sensor data to actions.
• Strong background in CV techniques, architectures, and real-time inference.

Responsibilities

• Design, develop, and deploy advanced onboard ML models for real-time inference in autonomous driving.
• Lead complex ML projects from conception to onboard implementation with scalability and safety focus.
• Advance architectures, training methods, and inference optimization for onboard use.
• Provide mentorship and raise ML engineering standards across teams.
• Collaborate with multidisciplinary groups to integrate ML into vehicle systems.
• Help shape ML roadmaps aligned with product milestones.

Skills

Advanced degree in ML/CS/EE

Foundation models & LLMs

End-to-end real-time onboard ML

Deep learning model development

Computer vision techniques

Python & C++

PyTorch / TensorFlow / JAX

Communication & mentoring

AV/ADAS experience

Education

Master's or Ph.D. in ML/Robotics/CS/EE

Tools

PyTorch

TensorFlow

JAX

Job description

General Motors is seeking a senior ML engineer to design, develop, and deploy onboard ML models for autonomous driving. You will lead end-to-end projects from concept to onboard implementation, focusing on real-time inference, robustness, and safety-critical operation.

You will advance neural networks, training, and optimization techniques, mentor teams, and collaborate across engineering groups to align ML roadmaps with vehicle product milestones.