Staff Machine Learning Engineer - ML Training InfrastructureGeneral Motors • Washington, Utah, United States

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Staff Machine Learning Engineer - ML Training Infrastructure

General Motors

Washington, Utah, United States

Washington, Utah, United States

À propos

The Role 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, trade‑off 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 trade‑offs 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) 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 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. Non-Discrimination and Equal Employment Opportunities (U.S.) General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers. All employment decisions are made on a non‑discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws. Remote Work This role is categorized as remote. The selected candidate may be based anywhere in the country of work and is not expected to report to a GM worksite unless directed by their manager.
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Washington, Utah, United States

Compétences linguistiques

English

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