Staff AI/ML Engineer, Embodied AI Foundations (Remote)

Aimlroles; Northern (KY); Hybrid; USD 208,000 - 281,000; Full time

Job summary

General Motors seeks a Staff AI/ML Engineer in the Embodied AI Data Foundations group to lead data-centric ML delivery for autonomous driving. You will leverage diverse real and synthetic datasets to train scalable models that improve driving safety and performance across real-world scenarios.

You will partner with engineering teams to translate research into scalable onboard and offboard ML solutions, mentoring junior engineers and shaping the autonomy stack as it evolves.

Qualifications

• Experience with large-scale foundation models and alignment methods.
• Demonstrated ability to deliver applied ML solutions under real-world constraints and timelines.
• Proficiency in PyTorch and Python.
• Experience deploying ML models into production environments and understanding end-to-end deployment workflows.

Responsibilities

• Design and implement ML solutions aligned with GM’s autonomous driving objectives.
• Apply techniques such as unsupervised pre-training, imitation learning, reinforcement learning, model scaling/selection, foundation modeling.
• Collaborate with cross-functional teams to deploy models and algorithms into onboard driving systems.
• Contribute to applied research efforts and remain current with advancements in ML frameworks and methods.
• Design and build efficient infrastructure, pipelines, and tooling to facilitate fast-pace model iterations.
• Drive technical execution from prototyping through production deployment, documenting learnings and best practices.
• Support and mentor engineers through technical collaboration and code reviews, fostering knowledge sharing and engineering excellence.

Skills

Foundation models

PyTorch

Python

Data pipelines

NumPy/Pandas

Spark

Deployment

Robotics/AV

Education

CS/Robotics/ML degree

Tools

Git

Job description

General Motors seeks a Staff AI/ML Engineer in the Embodied AI Data Foundations group to lead data-centric ML delivery for autonomous driving. You will leverage diverse real and synthetic datasets to train scalable models that improve driving safety and performance across real-world scenarios.

You will partner with engineering teams to translate research into scalable onboard and offboard ML solutions, mentoring junior engineers and shaping the autonomy stack as it evolves.