Staff Machine Learning Engineer, Compute

Description
Staff ML Engineer, ML Compute Platform
About the Team:
The ML Compute Platform is part of the AI Compute Platform organization within Infrastructure Platforms. Our team owns the cloud-agnostic, reliable, and cost-efficient compute backend that powers GM AI. We're proud to serve as the AI infrastructure platform for teams developing autonomous vehicles (L3/L4/L5), as well as other groups building AI-driven products for GM and its customers.
We enable rapid innovation and feature development by optimizing for high-priority, ML-centric use cases. Our platform supports the training and deployment of state-of-the-art (SOTA) machine learning models with a focus on performance, availability, concurrency, and scalability. We're committed to maximizing GPU utilization across platforms while maintaining reliability and cost efficiency.
About the Role:
We are looking for a Staff Software Engineer to join our team and help us scale our platform for performance, reliability, and usability. You'll be responsible for building critical backend services, integrating with GPU hardware and orchestration systems, and driving improvements to both system architecture and user experience.
This is a hands-on engineering role that requires a strong background in distributed systems, infrastructure, and a product mindset with a keen eye for user experience.
What you'll be doing:

• Design core platform backend software components

• Experience cloud platforms like GCP, Azure

• Thrive in a dynamic, multi-tasking environment with ever-evolving priorities. Interface with other teams to incorporate their innovations and vice versa

• Analyze and improve efficiency, scalability, and stability of various system resources

• Proactively identify, drive and design large initiatives across GM ML ecosystem

At a Minimum We'd Like You To Have

• 7+ years of industry experience

• Expertise in either Go, C++, Python or other relevant coding languages

• Strong background with kubernetes at scale

• Relevant experience building large-scale with distributed systems

• Experience leading and driving large scale initiatives

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

It's Preferred If You Have

• Hands-on experience in ML platforms

• Experience with GPU/TPU optimizations

• Experience with training frameworks like PyTorch, TorchX

• Experience with Ray framework

• Leadership/active participation in the open source community

• Experience infrastructure applications or similar experience

Why Join Us?
If you're excited to tackle some of today's most complex engineering challenges, see the impact of your work in real-world AV applications, and help shape the future of AI infrastructure at GM-this is the team for you.
Compensation:

• The expected base CA compensation for this role is: $198,900 - $304,800. Actual base compensation within the identified range will vary based on factors relevant to the position.
• You also need to include general information about potential commissions, if applicable.

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.
About GM
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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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