Staff Machine Learning Engineer - ML Training Infrastructure

We are seeking an experienced, technically strong, impact-driven expert in ML Training Infrastructure with a demonstrated ability to lead through hands-on technical work. In this role, you will be responsible for defining the technical direction and driving the design and development of scalable, reliable, and high-performance AI/ML platform infrastructure that enables advanced AI research and model development at scale.

As a Staff ML Engineer, you will operate as a technical leader across initiatives, partnering closely with machine learning engineers, research scientists, and platform teams to shape architecture, drive major technical decisions, and deliver state-of-the-art AI infrastructure that enables the future of intelligent driving technologies across General Motors vehicles.

What You'll Do: 

• 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.

Your Skills & Abilities (Required Qualifications)

• 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.

What Will Give You a Competitive Edge (preferred qualifications):

• 7+ years of professional software engineering experience.

• Deep expertise in PyTorch 2.x+ and distributed training frameworks.

• Experience designing and developing training platforms that support FSDP, pipeline parallelism, and other scalable solutions for training large foundational models.

• Experience profiling, analyzing, debugging, and optimizing training and data loading performance at scale.

• Strong record of technical leadership through architecture reviews, roadmap influence, and cross-team execution.

• Excellent communication skills, with the ability to build consensus, navigate controversial decisions, communicate risks clearly, and provide constructive technical feedback.

• Self-motivated, execution-oriented, and motivated by delivering broad organizational impact.

Compensation: The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of the California Bay Area. 

• The salary range for this role is $185,000 to $335,300. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position. 

• Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance. 

Relocation: This job may be eligible for relocation benefits.

Benefits: 

• Benefits: 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. Note: program participants are required to purchase/lease a qualifying GM vehicle every four years unless one of a limited number of exceptions applies.

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