Senior Machine Learning Engineer - ML Training Infrastructure

General Motors•Sunnyvale, CA

3d•$170,000 - $240,800•Hybrid

About The Position

General Motors is seeking an experienced, hands-on Senior Machine Learning Engineer specializing in ML training infrastructure. The role designs and builds scalable, reliable, high-performance AI/ML platform infrastructure to support advanced AI research and model development, including intelligent driving technologies across GM vehicles. The engineer collaborates with machine learning engineers, research scientists, and other partners to develop AI solutions. The position focuses on developing ML frameworks for training models at scale, improving observability, debuggability, operational excellence, and user experience, and analyzing and optimizing model-training performance. It also involves scaling distributed-training workflows, maximizing resource utilization across heterogeneous hardware, reducing costs, and integrating platform features and technologies with cross-functional teams. This role is categorized as hybrid. The selected candidate is expected to report to a specific location at least three times a week, or at another frequency determined by their manager, and travel less than 25% for the role. The posting states a salary range of $170,000 to $240,800, with actual pay dependent on position-related factors. The role may also be eligible for incentive pay based on company performance, job level, and individual performance.

Requirements

Bachelor's degree or higher in Computer Science or an equivalent major, or equivalent relevant experience.
At least 5 years of professional software engineering experience.
At least 1 year of specialized experience in AI/ML infrastructure, such as enabling distributed training to scale large ML models.
Strong Python programming skills and proficiency with PyTorch, TensorFlow, or a similar framework; PyTorch is preferred.
Experience with distributed computing, GPU computing, and cloud environments such as AWS, GCP, or Azure.
Willingness to travel to Sunnyvale, California, as needed.
Comfort working in highly ambiguous and dynamic environments.

Nice To Haves

Extensive knowledge and experience with PyTorch 2.x or later and distributed-training frameworks.
Experience designing and developing training frameworks supporting FSDP, Pipeline Parallelism, and other scalable approaches to training large foundational models.
Experience profiling, analyzing, debugging, and optimizing training and data-loading performance.
Strong execution and impact-delivery orientation; self-motivated.
Excellent communication skills, including resolving disagreements, building consensus, communicating risks, and giving constructive feedback.

Responsibilities

Design and develop scalable, reliable, high-performance ML frameworks to support model training at scale.
Improve system observability, debuggability, operational excellence, and user experience.
Analyze and optimize model-training performance; develop solutions to scale distributed-training workflows, maximize resource utilization across heterogeneous hardware environments, and reduce cost.
Collaborate with cross-functional teams to integrate new features and technologies into the platform.
Work with machine learning engineers, research scientists, and other partners to develop AI solutions supporting intelligent driving technologies.

Benefits

Medical, dental, and vision benefit options.
Health Savings Account and Flexible Spending Accounts.
Retirement savings plan.
Sickness and accident benefits.
Life insurance.
Paid vacation and holidays.
Tuition assistance programs.
Employee assistance program.
GM vehicle discounts.
Incentive pay program with payouts based on company performance, job level, and individual performance.
Relocation benefits for candidates who qualify under company policy.