Staff Machine Learning Engineer – ML Training Infrastructure

General Motors

Staff ML Engineer defining architecture and driving scalable ML infrastructure for AI at GM. Collaborating with teams to enhance intelligent driving technologies and optimize model training.

Posted 6/6/2026full-timeSunnyvale • California, Texas, Washington • 🇺🇸 United StatesLead💰 $185,000 - $335,300 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsGoogle Cloud PlatformPythonPyTorchTensorflow

About the role

Key responsibilities & impact

Define and drive the architecture, design, and development of scalable, reliable, and high-performance ML frameworks and platform capabilities to support model training at scale.
Lead model training performance analysis and optimization efforts across distributed training workflows, improving scalability, efficiency, and cost across heterogeneous hardware environments.
Raise the bar on system observability, debuggability, operational excellence, and developer experience across the ML training stack.
Own large, ambiguous, cross-functional technical initiatives from strategy through execution, including technical roadmap definition, tradeoff analysis, and delivery.
Influence platform direction by identifying long-term infrastructure investments, setting engineering standards, and driving adoption of best practices across teams.
Collaborate across organizational boundaries to align requirements, resolve technical disagreements, and integrate new capabilities into the platform ecosystem.
Mentor engineers through design reviews, technical guidance, and hands-on partnership, while elevating engineering quality across the team.

Requirements

What you’ll need

Bachelor's degree or higher in Computer Science or a related field, or equivalent practical experience.
7+ years of professional software engineering experience.
5+ years of specialized experience in AI/ML infrastructure, such as enabling distributed training for large-scale ML models.
Strong programming skills in Python, with deep proficiency in frameworks such as PyTorch (preferred), TensorFlow, or similar ML systems.
Proven experience designing and operating distributed systems for ML training, including distributed computing, GPU computing, and cloud environments (AWS, GCP, Azure).
Demonstrated track record of leading technically ambiguous, cross-team infrastructure initiatives and driving them to measurable impact.
Strong architectural judgment and ability to make sound technical tradeoffs across performance, reliability, usability, and cost.
Willingness to travel to Sunnyvale, CA as needed.
Comfortable operating in highly ambiguous and dynamic environments.

Benefits

Comp & perks

GM offers a variety of health and wellbeing benefit programs.
Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more.
Company Vehicle: Upon successful completion of a motor vehicle report review, you will be eligible to participate in a company vehicle evaluation program, through which you will be assigned a General Motors vehicle to drive and evaluate.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learningdistributed trainingPythonPyTorchTensorFlowdistributed systemsGPU computingcloud environmentsperformance analysisoptimization

Soft Skills

leadershipcollaborationmentoringtechnical guidanceproblem-solvingcommunicationstrategic thinkingadaptabilityinfluenceoperational excellence