Senior ML Infra Engineer for Autonomous Driving (Remote)

General Motors; Washington; Hybrid; USD 153,000 - 234,000; Full time

Benefits offered by this job

Bonus potential

Relocation benefits

Job summary

General Motors is seeking a Senior MLInfra Engineer to accelerate autonomous driving with scalable ML training and deployment platforms. You’ll build high-performance pipelines and tools enabling rapid dataset generation, training, and evaluation across GM's autonomous driving programs.

The role emphasizes robust APIs, cloud-based infrastructure, and collaboration with senior engineers to deliver reliable, scalable ML systems. Remote or hybrid work options are available, within the United States.

Qualifications

• 3+ years on large-scale distributed systems or ML infrastructure.
• Experience designing robust services with durable APIs.
• Hands-on ML workflows in production environments.
• Experience building scalable cloud-based systems.
• Experience with ML development lifecycle: training, deployment, MLOps.
• Strong cross-functional collaboration.

Responsibilities

• Design, implement, and deploy scalable ML training and evaluation platforms.
• Drive complex technical projects with ownership of implementation and reliability.
• Contribute to architectural decisions with senior engineers.
• Collaborate with partner teams to meet ML development needs.
• Mentor junior engineers and interns.
• Ensure high-quality code reviews and documentation.

Skills

Distributed systems

APIs design

Python or C++

Cloud infrastructure

ML infrastructure

Education

BS/MS/PhD in Computer Science or Mathematics

Tools

Docker

Kubernetes

PyTorch

TensorFlow

CMake

Job description

General Motors is seeking a Senior MLInfra Engineer to accelerate autonomous driving with scalable ML training and deployment platforms. You’ll build high-performance pipelines and tools enabling rapid dataset generation, training, and evaluation across GM's autonomous driving programs.

The role emphasizes robust APIs, cloud-based infrastructure, and collaboration with senior engineers to deliver reliable, scalable ML systems. Remote or hybrid work options are available, within the United States.