Senior ML Infrastructure Engineer - Embodied AI Scaling Foundations

Job DescriptionAt General Motors, our product teams are redefining mobility. Through a human-centered design process, we create vehicles and experiences that are designed not just to be seen, but to be felt. We’re turning today’s impossible into tomorrow’s standard —from breakthrough hardware and battery systems to intuitive design, intelligent software, and next-generation safety and entertainment features. Every day, our products move millions of people as we aim to make driving safer, smarter, and more connected, shaping the future of transportation on a global scale.Are you passionate about accelerating the future of autonomous driving? Join the Embodied AI Infra Foundation team at General Motors, where we build the critical infrastructure that powers every machine learning engineer working on our cutting-edge Autonomous Driving models. From foundational models to state-of-the-art optimization, our work is at the heart of our mission.About the team:Our goal is simple: dramatically accelerate the machine learning development cycle, freeing our engineers to focus entirely on enhancing the safety and performance of the car, rather than managing infrastructure. We are committed to delivering products that are performant, easy to use, and exceptionally reliable. Your success will be measured by the success of our partner teams who rely on our robust systems to build the world's most advanced driverless vehicles.As a Senior ML Infra Engineer, you will build critical infrastructure that powers every machine learning engineer working on our cutting-edge Autonomous Driving models. From foundational models to state-of-the-art optimization, our goal is simple: dramatically accelerate the machine learning development cycle. We are committed to delivering products that are performant, easy to use, and exceptionally reliable. Your success will be measured by the success of our partner teams who rely on our robust systems to build the world's most advanced driverless vehicles.What you'll do: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. You will be a core contributor to team planning, design reviews, and code quality.Take a holistic view of projects, considering their impact across multiple teams, and proactively drive technical prioritization. Collaborate closely with partner teams to ensure maximum benefit from the systems we build.Help shape our team through technical interviewing with high, well-calibrated standards, and play an essential role in recruiting. Mentor and onboard junior engineers and interns, helping them grow their careers.Your Skills & Abilities (Required 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.Demonstrated ability to collaborate effectively across multiple teams and organizations.Proficiency working with containerization and orchestration technologies (Docker, Kubernetes).A strong passion for self-driving technology and its transformative potential.Exceptional coding skills in Python or C++.BS, MS, or PhD in Computer Science, Math, or equivalent practical experience.Exceptional candidates may also have:Experience with distributed training methodologies.A background in optimizing model training performance.Experience scaling model training across large clusters of GPUs/CPUs or other accelerators.Familiarity with deep learning frameworks such as PyTorch, TensorFlow, etc.A strong grasp of performance profiling and state-of-the-art training optimization algorithms, including their performance characteristics and effect on model convergence.Experience with advanced build systems (Bazel, Buck, Blaze, or Cmake).3+ years of professional experienceRemote/Hybrid: This role is based remotely but if you live within a 50-mile radius of an office, you are expected to report to that location three times a week, at minimum. 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 $128,700.oo to $261,300.00. 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. 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. Relocation: This job may be eligible for relocation benefits. #GM-AV-1#LI-CX1 This role is based remotely, but if the selected candidate lives within a specific mile radius of a GM hub, they will be expected to report to the location three times a week {or other frequency dictated by your manager}. This job may be eligible for relocation benefits. About GMOur vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.Why Join UsWe believe we all must make a choice every day – individually and collectively – to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team.Benefits OverviewFrom day one, we're looking out for your well-being–at work and at home–so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources.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. We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire.AccommodationsGeneral Motors offers opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, emailus or call us at View phone number on us.fitly.work. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.SummaryLocation: Sunnyvale, California, United States of America; Remote - Washington; Austin, Texas, United States of AmericaType: Full time