JOB DESCRIPTION

Hybrid: This role is categorized as hybrid. This means the successful candidate is expected to report to the Mountain View office three times per week, at minimum.
Join the team driving the future of AI innovation at General Motors! Our AI Core Infrastructure team, which sits directly under the Chief AI Officer, is a high-impact team powering GM’s enterprise-wide AI infrastructure—from customer in-vehicle experience to manufacturing innovation. We design the foundational AI infrastructure that powers every corner of the company. We’re building the data, compute, and deployment layers necessary to scale cutting-edge AI systems across GM’s global operations.
Our core responsibilities include building and scaling core AI infrastructure and tools to enable rapid development, training, fine-tuning, evaluation, and deployment of AI models across the organization, collaborating with vehicle and manufacturing teams to deliver high-impact AI solutions across GM’s portfolio, championing and promoting cutting-edge AI technologies, while ensuring responsible AI practices.

YOUR SKILLS & ABILITIES (REQUIRED QUALIFICATIONS)

• 7+ years in software engineering or ML infrastructure (or post-graduate equivalent)
• Proven track record of building cutting-edge AI infrastructure (data, training, eval and/or serving)
• Strong understanding of modern AI algorithms, models and frameworks (e.g. PyTorch, Tensorflow etc.)
• Experience deploying AI systems on Cloud-based GPUs/TPUs (AWS, Azure, GCP, etc.)
• Fluency in Python, and at least one of C++/Go/Java
• Proven ability to architect production-ready distributed systems
• Highly developed communication and collaboration skills

WHAT WILL GIVE YOU A COMPETITIVE EDGE (PREFERRED QUALIFICATIONS)

• Experience in the automotive industry or working with physics-based simulations
• Experience with natural language AI, such as customization, summarization and translation
• Experience with test-driven development

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 186,000 to 285,000. 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.

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.
Relocation: This job may be eligible for relocation benefits.

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THE ROLE:

As a Staff Machine Learning Engineer on the AI Core Infrastructure team, you will play a critical role in shaping GM’s future through the high-impact AI systems you develop. You will design and build advanced AI technologies then integrate them into GM’s business strategies, culture, and operational workflows. You’ll lead large-scale infrastructure initiatives and set technical direction across projects. You’ll serve as a key technical partner to research, product, and vehicle teams, mentoring engineers and driving system architecture.

WHAT YOU’LL DO:

• Design, develop and test cutting-edge AI infrastructure, including data pipelines, fine-tuning frameworks and evaluation tools
• Collaborate with researchers and industry partners to understand technical requirements and measure impact
• Scale our architecture to deploy reliably and performantly across large-scale applications
• Define best practices for AI software engineering across the company, including AI safety/guardrails
• Provide technical guidance to internal teams and business partners
• Lead cross-functional development efforts with engineering and product teams
• Mentor engineers and foster technical growth across the teams