Remote Staff AI Research Scientist, Autonomous Driving

generalmotors; Sunnyvale (CA); Remote; USD 219,000 - 335,000; Full time

Benefits offered by this job

Relocation support

GM benefits package

Job summary

General Motors is seeking a Staff Research Scientist in the AI Research organization to advance machine learning for autonomous driving. You will design state-of-the-art algorithms, mentor teams, and publish at top conferences.

You will influence roadmaps, collaborate with engineering groups, and represent GM through publications and patents, helping shape the future of mobility technology. Remote role with on-site collaboration near Mountain View; leadership and cross-disciplinary impact are

Qualifications

• Ph.D. in ML, robotics, CS, EE, or related field.
• 5+ years of AI/ML research and applied development.
• Deep expertise in modern ML architectures (transformers, generative AI, multimodal systems).
• Excellent Python programming skills and ability to mentor across the org.

Responsibilities

• Research, design, and prototype advanced ML approaches to autonomous driving.
• Influence technical roadmaps and ML priorities aligned with company objectives.
• Provide technical mentorship and vision.
• Collaborate with multidisciplinary engineering groups to foster ML innovation.
• Represent GM through publications and patents.

Skills

Ph.D. in ML/related field

5+ years AI/ML research

Transformers, generative AI

Mentoring & communication

Education

Ph.D. in Machine Learning/Robotics/CS/EE

Tools

Python

PyTorch/TensorFlow

Job description

General Motors is seeking a Staff Research Scientist in the AI Research organization to advance machine learning for autonomous driving. You will design state-of-the-art algorithms, mentor teams, and publish at top conferences.

You will influence roadmaps, collaborate with engineering groups, and represent GM through publications and patents, helping shape the future of mobility technology. Remote role with on-site collaboration near Mountain View; leadership and cross-disciplinary impact are