Staff ML Engineer: Scalable AI Platforms for Autonomy

General Motors; Sunnyvale (CA); Hybrid; USD 189,000 - 300,000; Full time

Benefits offered by this job

Health benefits

Company vehicle program

Relocation assistance

Job summary

General Motors is seeking a Staff ML Engineer to build scalable infrastructure powering autonomous driving ML workflows. You will lead design and deployment of platforms enabling model training and evaluation across GM, while guiding architectural decisions and trade-offs.

You will collaborate across teams, mentor junior engineers, and shape recruitment standards. A strong background in distributed ML systems, APIs, and cloud-native tools is required, with expertise in Python or C++.

Qualifications

• 5+ years of experience building large-scale distributed systems, applications, or advanced ML systems.
• Hands-on experience with ML development lifecycle and MLOps practices.
• Strong APIs design with durable, high-quality interfaces.
• Proficient in cloud infrastructure for scalable ML workloads.
• Solid Python or C++ coding skills and performance awareness.
• Excellent cross-functional collaboration across teams and organizations.

Responsibilities

• Lead design, implementation, and deployment of scalable ML platforms and tools for model training and evaluation workflows.
• Own complex projects end-to-end, making architectural decisions and trade-offs.
• Collaborate with partner teams to maximize system impact and prioritize work.

Skills

Distributed systems

ML systems

APIs design

Cloud infrastructure

MLOps

Cross-functional collaboration

Containerization

Python or C++

Education

BS/MS/PhD in CS/Math

Tools

Docker

Kubernetes

Bazel

CMake

Job description

General Motors is seeking a Staff ML Engineer to build scalable infrastructure powering autonomous driving ML workflows. You will lead design and deployment of platforms enabling model training and evaluation across GM, while guiding architectural decisions and trade-offs.

You will collaborate across teams, mentor junior engineers, and shape recruitment standards. A strong background in distributed ML systems, APIs, and cloud-native tools is required, with expertise in Python or C++.