Job Description

We’re General Motors (GM), a company driving the future of mobility with advanced self-driving and electric vehicle technologies.

We’re building the world’s most innovative autonomous vehicles to safely connect people to the places, things, and experiences they care about. We believe self-driving vehicles will help save lives, reshape cities, give back time in transit, and restore freedom of movement for many.

GM employees have the opportunity to grow and develop while learning from leaders at the forefront of their fields. With a culture of internal mobility, there’s an opportunity to thrive in a variety of disciplines. This is a place for dreamers and doers to succeed.

If you are looking to play a part in making a positive impact in the world by advancing the revolutionary work of self-driving vehicles, join us.

About the team:

The AV ML Infra team at GM builds ML infrastructure designed to meet the unique demands of AI and ML innovation, supporting a wide range of use cases across teams such as Embodied AI, Simulation, Data Science, and more. We enable scalable and efficient ML experimentation, enhance the productivity of ML engineers, and drive the adoption of cutting-edge ML techniques.

Our ML infrastructure includes:

• AI Validation & Inference: Ensures robust model performance by running large-scale simulation workloads and managing reliable ML inference pipelines. 
   

• ML Compute: Streamlines and optimizes large-scale ML training and inference across cloud and on-prem compute resources. 
   

• AV Pipelines & Lineage: Orchestrates ML workflows while tracking data and model lineage across diverse infrastructures, accelerating engineering velocity and ensuring reproducibility. 

Together, these tools and systems empower GM to tackle the complexities of autonomous driving technology and expedite our path to commercialization.

Position Overview:

The Principal AI/ML Engineer will lead a growing organization, guiding the AV ML Infra team in achieving its mission while shaping long-term vision and execution strategies across GM’s AI and ML efforts. This leadership role will drive a transformative leap in our infrastructure capabilities to meet the scale we anticipate. The leader will articulate a clear vision and strategy, design an organizational structure to enable effective execution, and cultivate strong partnerships across cross-functional teams and stakeholders.

Note: This role is part of an ML infrastructure engineering team and does not involve applying machine learning models for specific tasks. The focus is on developing infrastructure products that empower GM teams to perform machine learning and data science at scale.

What you’ll be doing:

• Design & Implementation: Utilize the latest cloud technologies (GCP/Azure) to design, implement, and test scalable distributed computing and data processing solutions in the cloud. 

• Project Ownership: Take ownership of technical projects from inception to completion, contribute to the product roadmap, and make informed decisions on major technical trade-offs. 

• Collaboration: Engage effectively in team planning, code reviews, and design discussions, considering the impact of projects across multiple teams while proactively managing conflicts. 

• Mentorship & Recruitment: Conduct technical interviews with calibrated standards, onboard, and mentor engineers and interns, fostering a culture of growth and knowledge sharing. 

What you must have:

• 10+ years of experience, with a strong background in large-scale distributed systems preferred. 

• 5+ years of experience leading and driving large-scale initiatives. 

• Proficiency in building scalable infrastructure on the cloud using Python, C++, Golang, or similar languages. 

• Experience working with relational and NoSQL databases. 

• Demonstrated ability to develop and maintain systems at scale. 

• A Bachelor’s, Master’s, or Ph.D. in Computer Science, Electrical Engineering, Mathematics, Physics, or a related field; or equivalent practical experience. 

• A passion for autonomous vehicle technology and its transformative potential. 

• Strong attention to detail and a commitment to accuracy. 

• A proven track record of efficiently solving complex problems. 

• A startup mentality with a willingness to embrace uncertainty and wear multiple hats. 

Bonus Points:

• Experience with Google Cloud Platform, Microsoft Azure, or Amazon Web Services. 

• Experience with open-source orchestration platforms such as Kubeflow, Flyte, Airflow, etc. 

• Experience with model serving frameworks such as RayServe, vLLM, Triton. 

• Experience with Kubernetes. 

• Understanding of Machine Learning (ML) models/pipelines. 

• Python/C++/Golang proficiency. 

• Relevant publications. 

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 New York, Colorado, California, or Washington.

• The salary range for this role is $275,800 to $340,500. 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: 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.  

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.

Remote/Hybrid: This role is based remotely but if you live within a 50-mile radius of Mountain View, you are expected to report to that location three times a week, at minimum.







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.

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.

Accommodations

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