Sobre este puesto de Senior ML Infrastructure Engineer - Embodied AI en General Motors
Job Description
At 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.
Role:
Are you passionate about accelerating the future of autonomous driving? Join the Embodied AI team at General Motors. Our team is developing and deploying machine learning solutions that support safe and reliable autonomous vehicle behavior across real-world scenarios.
As a Senior ML Infra Engineer, you will work on the core systems that enable rapid dataset generation, training, evaluation and iteration of our most advanced Autonomous Driving models. From enabling large foundational driving models to distilling multi-stage production deployed models, your goal will be to dramatically accelerate the machine learning development cycle from one modeling hypothesis to next.
You will develop model training pipelines that are performant, easy to use, and exceptionally reliable. Your success will be measured by the velocity and impact of the ML models that rely on the scalable, intuitive, and high‑performance training platforms you help create.
What You’ll Do:
Design, implement, and deploy scalable platforms and tools supporting machine learning training and evaluation workflows across GM.
Drive complex technical projects with strong ownership of implementation, code quality, and system reliability.
Contribute to technical design discussions and architectural decisions while collaborating with senior engineers and technical leads.
Work closely with partner teams to ensure platforms meet real-world ML development needs and maximize adoption.
Identify technical improvements and help prioritize platform investments to improve performance, reliability, and developer productivity.
Contribute to a strong engineering culture through high-quality code reviews, documentation, and operational excellence.
Support onboarding and mentoring of junior engineers and interns.
What You’ll Bring:
3+ years of experience working on large-scale distributed systems, applications, or ML infrastructure.
Experience designing robust services or frameworks with durable, well-designed APIs.
Solid understanding of machine learning workflows and hands-on experience applying ML systems in production environments.
Experience building reliable, high-performance, and cost-efficient systems on modern cloud infrastructure.
Practical experience across the ML development lifecycle, including model training, deployment, and MLOps practices.
Strong cross-functional collaboration skills across teams and organizations.
Strong coding skills in Python or C++.
Interest in autonomous driving and large-scale ML systems.
BS, MS, or PhD in Computer Science, Mathematics, or equivalent practical experience.
Nice to Have:
Experience with distributed training methodologies.
Experience scaling ML training across large GPU/CPU clusters or specialized accelerators.
Familiarity with deep learning frameworks such as PyTorch or TensorFlow.
Experience with performance profiling and training optimization techniques and their impact on model convergence and performance.
Experience with advanced build systems such as Bazel, Buck, Blaze, or CMake.
Proficiency with containerization and orchestration technologies (e.g., Docker, Kubernetes).
Remote/Hybrid: This role is categorized as fully remote or hybrid.
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 $153,200.00 to $234,100.00. 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.
This job may be eligible for relocation benefits.
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About GM
Our 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 Us
We 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 Overview
From 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.
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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.
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