Description The Role: We are seeking an experienced, technical oriented, impact delivering-driven expert in ML Training Infrastructure with a strong ability to execute hands-on technical work. In this role, you will be responsible for designing and building scalable, reliable, and high-performance AI/ML platform infrastructure to support advanced AI research and model development initiatives. As a Senior ML Engineer, you will collaborate closely with machine learning engineers, research scientists, and other partners to develop state-of-the-art AI solutions that enable the future of intelligent driving technologies across General Motors vehicles. What You'll Do: - Design and development of scalable, reliable, high-performance ML framework to support model training at scale. - Model training performance analysis and optimization solutions to scale distributed training workflows and maximize resource utilization across heterogeneous hardware environments, and save cost. - Raise the bar on system observability, debuggability, and operational excellence, and user experience. - Collaborate with cross-functional teams to integrate new features and technologies into the platform. Your Skills & Abilities (Required Qualifications) - Bachelors degree or higher in Computer Science or equivalent major OR equivalent relevant experience - 3+ years professional software engineering experience - 2+ years specialized experience in AI/ML infrastructure, e.g., enabling distributed training for scaling large ML models - Strong programming skills in Python, with proficiency in frameworks such as,PyTorch (preferred), TensorFlow, or similar - Experience with distributed computing, GPU computing, and cloud environments (AWS, GCP, Azure). - Willingness to travel to Sunnyvale, CA as needed - Comfortable working in highly ambiguous and dynamic environments What Will Give You a Competitive Edge (preferred qualifications): - 5+ years of professional software engineering experience. - Self-motivated, strong execution, impact-delivering oriented - Extensive knowledge and experience with PyTorch 2.x+ and distributed training framework - Experience with design and development of training framework that supports FSDP, Pipeline Parallelism and other scalable solutions to training large foundational models - Experience with profiling, analysis, debugging and optimizing training and data loading performance. - Excellent communication skills to resolve controversial, make consensus, communicate risks and give constructive feedback 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 the California Bay Area. - The salary range for this role is $170,000 to $240,000. 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. Relocation: This job may be eligible for relocation benefits. Benefits: - 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. #GM-AV-1 This role is based remotely, but if the selected candidate lives within a specific mile radius of a GM hub, they will be expected to report to the location three times a week {or other frequency dictated by your manager}. The selected candidate will be required to travel <25% for this role. This job may be eligible for relocation benefits. 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. Why Join Us We believe we all must make a choice every day - individually and collectively - to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team. Total Rewards | Benefits Overview From day one, we're looking out for your well-being-at work and at home-so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources. 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. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers. 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. 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. Accommodations 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, email us [email protected] or call us at 1-800-865-7580. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.

• Build and maintain automated CI/CD and CT pipelines across AWS and Azure • Design and execute the infrastructure for Retrieval-Augmented Generation (RAG) • Build engineering pipes to securely ingest data from legacy systems into cloud-native MLOps workflows • Implement systemized frameworks for LLM evaluation and traditional ML validation • Deploy real-time monitoring for model drift and quality management • Manage all AI resources using Terraform or CloudFormation • Partner with teams using platforms like Palantir and Databricks • Work with central IT and Security to navigate IAM roles and configurations • Optimize model serving endpoints for high-throughput and low-latency • Establish rigorous version control for model weights and data snapshots • Support automation of feature stores and pipelines • Implement automated scanning and guardrails to prevent data leakage

• Use the latest cutting edge technologies such as LLMS, multimodal models to handle complex problems. • Work with large datasets, perform data preprocessing, and engineer relevant features to enhance model performance. • Build frameworks that allow us to iterate and evaluate model versions (ranking, accuracy, latency). • Deploy Models at Scale: Collaborate with software engineers to deploy machine learning models into production, ensuring seamless integration with existing systems. • Monitoring and Maintenance: Implement monitoring solutions to track model performance in real-time and perform regular maintenance and updates as needed. • Collaboration: Work closely with cross-functional teams, including data scientists, software developers, and business analysts, to understand requirements and deliver impactful solutions. • Research and Innovation: Stay abreast of the latest advancements in machine learning and contribute to the research and development of innovative solutions.

• Design, implement, and train discrete and continuous diffusion models for predicting biomolecular structure tokens • Develop and iterate on structure tokenizers, including vector-quantized representations of 3D molecular and protein structure • Build and maintain data processing pipelines for large-scale biomolecular structure datasets • Train models on multi-GPU clusters, managing large-scale training runs • Develop rigorous benchmarking and evaluation workflows; validate against external benchmarks while prioritizing internal discovery-relevant metrics • Collaborate with ML scientists, computational chemists, and drug discovery teams to integrate models into discovery workflows • Communicate results to internal teams, external partners, and at scientific conferences • Mentor interns and junior team members through code reviews, technical guidance, and best practices (Senior level)