The Role: We are seeking a Principal AI Engineer to lead the design and advancement of our AI platform. You will play a key role in shaping the infrastructure that powers large-scale training and cloud inference. This includes accelerating training throughput, scaling multi-modal models, and enabling the next generation of AI-driven driving systems. We're tackling challenges across distributed training, training efficiency, DDP/FSDP, data processing pipelines, and Pytorch model optimization. This is a highly impactful position where your technical leadership will define how we scale AI to achieve autonomy. What You’ll Do: Architect, build, and optimize core AI/ML platform infrastructure to support massive-scale model training. Collaborate with data scientists, ML engineers, and software developers to enable seamless workflows from research to production. Drive efficiency in large-scale distributed training and data processing pipelines. Establish best practices for reliability, scalability, and performance across the AI/ML platform. Provide technical leadership and mentorship, guiding teams on platform design, architecture decisions, and emerging technologies. Partner with cross-functional stakeholders to align platform capabilities with business needs and strategic AI initiatives. Your Skills & Abilities (Required Qualifications): Bachelor’s degree or higher in Computer Science, related field, or equivalent experience. 8+ years of professional software engineering experience. 4+ years of specialized experience in AI/ML domain (e.g., enabling distributed training for large-scale models). Strong programming skills in Python, with proficiency in frameworks such as PyTorch (preferred) or TensorFlow. Experience with distributed systems, GPU computing, and cloud environments (AWS, GCP, or Azure). Comfortable operating in highly ambiguous and dynamic environments. Willingness to travel to Sunnyvale, CA as needed. What Will Give You a Competitive Edge (Preferred Qualifications): Proven track record of self-motivation, execution, and delivering impact. Deep expertise with PyTorch 2.x+ and distributed training frameworks. Strong skills in profiling, analysis, debugging, and optimizing training performance (e.g., avoiding memory fragmentation, operation fusion). Proficiency in C++ for performance-critical components. Experience leading cross-functional projects and aligning diverse stakeholders on priorities. Compensation: The salary range for this role is $197,600 to $374,200. 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, including 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. This role is based remotely but may require reporting to a location within a 50-mile radius of certain cities. 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. 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. 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. General Motors offers opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, please contact us.

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