Senior ML Infra Engineer — Embodied AI Foundations

General Motors; Sunnyvale (CA); Hybrid; USD 153,000 - 234,000; Full time

Benefits offered by this job

Health benefits

Relocation assistance

GM vehicle discounts

Job summary

General Motors is seeking an Senior ML Infra Engineer to accelerate autonomous driving infrastructure. You will lead scalable ML training workflows, own end-to-end projects, and mentor junior engineers across multiple teams.

You will collaborate with cross-functional partners, leverage Docker and Kubernetes in cloud environments, and contribute to building robust, high-performance systems for cutting-edge driverless technology.

Qualifications

• 3+ years of experience building large-scale distributed systems/applications or advanced ML Applications.
• Proven track record of building robust frameworks with high-quality, long-lasting APIs.
• Deep understanding and practical experience with machine learning algorithms.
• Expertise in building reliable, highly performant, and cost-efficient systems leveraging modern cloud infrastructure.
• Hands-on experience with the entire ML development lifecycle and MLOps practices.

Responsibilities

• Lead the design, implementation, and deployment of scalable platforms and tools that drive machine learning model training and evaluation workflows across GM.
• Own complex technical projects end-to-end, making key architectural decisions and technical trade-offs.
• Take a holistic view of projects, considering their impact across multiple teams, and proactively drive technical prioritization.
• Collaborate with partner teams to ensure maximum benefit from the systems we build, and help shape our team through technical interviewing and mentoring.

Skills

Distributed systems

ML applications

APIs design

Cloud infrastructure

MLOps

Cross-team collaboration

Python or C++

Education

BS/MS/PhD in CS/Math or equivalent

Tools

Docker

Kubernetes

PyTorch/TensorFlow

Job description

General Motors is seeking an Senior ML Infra Engineer to accelerate autonomous driving infrastructure. You will lead scalable ML training workflows, own end-to-end projects, and mentor junior engineers across multiple teams.

You will collaborate with cross-functional partners, leverage Docker and Kubernetes in cloud environments, and contribute to building robust, high-performance systems for cutting-edge driverless technology.