Staff AI/ML Engineer - Autonomous Driving (Remote)

generalmotors; Mountain View (CA); Hybrid; USD 219,000 - 335,000; Full time

Benefits offered by this job

Health benefits

GM vehicle discounts

Relocation benefits

Job summary

General Motors seeks a Staff AI/ML Engineer in the Embodied AI group to develop and deploy machine learning solutions that enhance autonomous driving performance. You will design advanced ML models for safe, reliable vehicle behavior across diverse real-world scenarios.

You will mentor engineers, translate research into scalable onboard systems, and drive production deployment from prototyping to deployment while collaborating with cross-functional teams.

Qualifications

• Bachelor’s, Master’s, or PhD in Computer Science, Robotics, Machine Learning, or related field.
• Experience with large-scale foundation models and alignment methods in real-world systems.
• Proficiency in PyTorch and Python; experience building training pipelines and data processing with NumPy/Pandas/Spark.

Responsibilities

• Design and implement ML solutions aligned with GM’s autonomous driving objectives.
• Apply techniques such as unsupervised pre-training, imitation learning, reinforcement learning, and model scaling.
• Collaborate with cross-functional teams to deploy models into onboard driving systems.
• Contribute to applied research and stay current with ML frameworks and methods.
• Design and build infrastructure, pipelines, and tooling for fast model iteration.
• Drive from prototyping to production deployment and document learnings.

Skills

PyTorch

Python

NumPy

Pandas

Apache Spark

Education

Bachelor’s, Master’s, or PhD in Computer Science, Robotics, Machine Learning

Job description

General Motors seeks a Staff AI/ML Engineer in the Embodied AI group to develop and deploy machine learning solutions that enhance autonomous driving performance. You will design advanced ML models for safe, reliable vehicle behavior across diverse real-world scenarios.

You will mentor engineers, translate research into scalable onboard systems, and drive production deployment from prototyping to deployment while collaborating with cross-functional teams.