Machine Learning Engineer - Remote

About The Company

General Motors (GM) is a global leader in automotive manufacturing and innovation, dedicated to shaping the future of mobility. With a rich history of technological advancements and a commitment to sustainability, GM strives to create safer, cleaner, and more efficient transportation solutions. The company's vision is centered around achieving Zero Crashes, Zero Emissions, and Zero Congestion, reflecting its dedication to making the world a better place through cutting-edge technology and responsible corporate practices. GM operates across multiple markets worldwide, leveraging a diverse portfolio of brands and a strong focus on research and development to stay at the forefront of the automotive industry.

About The Role

We are seeking an experienced and technically proficient Staff ML Engineer specializing in Machine Learning Training Infrastructure. This role is pivotal in defining and leading the development of scalable, reliable, and high-performance AI/ML platform infrastructure that supports advanced model training and research. As a key technical leader, you will collaborate with cross-functional teams, including machine learning engineers, research scientists, and platform specialists, to shape the architecture of our AI training systems. Your expertise will drive innovation in distributed training frameworks, optimize performance across heterogeneous hardware environments, and influence long-term infrastructure strategies. This position offers an exciting opportunity to impact the future of intelligent driving technologies within GM’s portfolio, contributing to the development of autonomous vehicle systems and other AI-powered innovations.

Qualifications

• Bachelor's degree or higher in Computer Science, Electrical Engineering, or a related field, or equivalent practical experience.
• 7+ years of professional software engineering experience.
• 5+ years of specialized experience in AI/ML infrastructure, including enabling distributed training for large-scale models.
• Strong programming skills in Python, with proficiency in frameworks such as PyTorch (preferred), TensorFlow, or similar ML systems.
• Experience designing and operating distributed systems for machine learning training, including distributed computing, GPU acceleration, and cloud environments (AWS, GCP, Azure).
• Proven track record of leading cross-team infrastructure initiatives and delivering measurable impact.
• Excellent architectural judgment with the ability to make technical tradeoffs balancing performance, reliability, usability, and cost.
• Willingness to travel to Sunnyvale, CA, as required.
• Comfortable working in dynamic, ambiguous environments with minimal supervision.

Responsibilities

• Define, design, and develop scalable, reliable, and high-performance machine learning frameworks and platform capabilities to support large-scale model training.
• Lead performance analysis and optimization of distributed training workflows to enhance scalability, efficiency, and cost-effectiveness across diverse hardware architectures.
• Enhance system observability, debuggability, and operational excellence within the ML training stack to improve developer experience and system reliability.
• Own and drive large, ambiguous, cross-functional technical initiatives from strategy to execution, including defining roadmaps, analyzing tradeoffs, and delivering solutions.
• Influence platform architecture by identifying long-term infrastructure investments, establishing engineering standards, and promoting best practices across teams.
• Collaborate with various organizational units to align requirements, resolve technical disagreements, and integrate new capabilities into the platform ecosystem.
• Mentor engineering teams through design reviews, technical guidance, and hands-on collaboration to elevate engineering quality and technical expertise.

Benefits

• Comprehensive health and wellbeing programs including medical, dental, and vision coverage.
• Retirement savings plans and financial wellness benefits.
• Paid vacation, holidays, and sick leave to support work-life balance.
• Tuition reimbursement and professional development opportunities.
• Employee assistance programs and mental health resources.
• GM vehicle discounts and potential participation in the company vehicle evaluation program.
• Relocation benefits may be available for eligible candidates.