Description The Role: We are seeking an experienced, technically strong, impact-driven expert in ML Training Infrastructure with a demonstrated ability to lead through hands-on technical work. In this role, you will be responsible for defining the technical direction and driving the design and development of scalable, reliable, and high-performance AI/ML platform infrastructure that enables advanced AI research and model development at scale. As a Staff ML Engineer, you will operate as a technical leader across initiatives, partnering closely with machine learning engineers, research scientists, and platform teams to shape architecture, drive major technical decisions, and deliver state-of-the-art AI infrastructure that enables the future of intelligent driving technologies across General Motors vehicles. What You'll Do: - Define and drive the architecture, design, and development of scalable, reliable, and high-performance ML frameworks and platform capabilities to support model training at scale. - Lead model training performance analysis and optimization efforts across distributed training workflows, improving scalability, efficiency, and cost across heterogeneous hardware environments. - Raise the bar on system observability, debuggability, operational excellence, and developer experience across the ML training stack. - Own large, ambiguous, cross-functional technical initiatives from strategy through execution, including technical roadmap definition, tradeoff analysis, and delivery. - Influence platform direction by identifying long-term infrastructure investments, setting engineering standards, and driving adoption of best practices across teams. - Collaborate across organizational boundaries to align requirements, resolve technical disagreements, and integrate new capabilities into the platform ecosystem. - Mentor engineers through design reviews, technical guidance, and hands-on partnership, while elevating engineering quality across the team. Your Skills & Abilities (Required Qualifications) - Bachelor's degree or higher in Computer Science or a related field, or equivalent practical experience. - 8+ years of professional software engineering experience. - 5+ years of specialized experience in AI/ML infrastructure, such as enabling distributed training for large-scale ML models. - Strong programming skills in Python, with deep proficiency in frameworks such as PyTorch (preferred), TensorFlow, or similar ML systems. - Proven experience designing and operating distributed systems for ML training, including distributed computing, GPU computing, and cloud environments (AWS, GCP, Azure). - Demonstrated track record of leading technically ambiguous, cross-team infrastructure initiatives and driving them to measurable impact. - Strong architectural judgment and ability to make sound technical tradeoffs across performance, reliability, usability, and cost. - Willingness to travel to Sunnyvale, CA as needed. - Comfortable operating in highly ambiguous and dynamic environments. What Will Give You a Competitive Edge (preferred qualifications): - 8+ years of professional software engineering experience. - Deep expertise in PyTorch 2.x+ and distributed training frameworks. - Experience designing and developing training platforms that support FSDP, pipeline parallelism, and other scalable solutions for training large foundational models. - Experience profiling, analyzing, debugging, and optimizing training and data loading performance at scale. - Strong record of technical leadership through architecture reviews, roadmap influence, and cross-team execution. - Excellent communication skills, with the ability to build consensus, navigate controversial decisions, communicate risks clearly, and provide constructive technical feedback. - Self-motivated, execution-oriented, and motivated by delivering broad organizational impact. 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 $185,000 to $335,300. 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. 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. #GM-AV-1 This role is categorized as remote. This means the selected candidate may be based anywhere in the country of work and is not expected to report to a GM worksite unless directed by their manager. 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.

Role Description As an MLOps Engineer, you will focus on optimizing, deploying, and operating large-scale machine learning models that power Boltz Lab. Your primary responsibility will be to ensure that advanced models for molecular modeling and design run efficiently, reliably, and cost-effectively across distributed systems. - Work closely with ML Researchers to turn trained models into production-ready services. - Optimize training and inference performance, reducing memory and compute overhead. - Scale workloads across multi-GPU and cloud environments. - Profile, improve model throughput and latency. - Harden systems for long-running and high-volume workloads. This role is ideal for someone who thrives on technical ownership and operational excellence, enjoys working close to systems and infrastructure, and is motivated by deploying high-impact machine learning systems at scale for real-world scientific use. Qualifications - 5+ years of experience in industry. - Strong experience deploying and operating machine learning models in production environments. - Proven ability to optimize training and inference workloads, including profiling performance, reducing memory and compute usage, and improving throughput and latency. - Hands-on experience with distributed frameworks and tooling. - Hands-on experience with PyTorch and the scientific Python ecosystem. - Strong understanding of MLOps best practices, including experiment tracking, model versioning, reproducibility, and CI/CD for ML systems. - Strong software engineering fundamentals, with experience building reliable, well-tested, and maintainable ML infrastructure. - Comfortable collaborating closely with ML researchers to translate research models into robust production services. Requirements - Exposure to computational biology or chemistry workflows and data formats. - Background working with large-scale scientific or numerical workloads. - Experience operating ML systems under real-world constraints such as cost, latency, and reliability. Benefits - Opportunity to drive outsized real-world impact by building tools that empower thousands of scientists across the industry. - Work alongside one of the most talent-dense teams in the field. - Significant ownership and independence, with responsibility for driving projects from concept to deployment. - Highly competitive salary with substantial equity ownership.

• Optimize Inference: Build and tune production LLM serving with vLLM and SGLang • Profile & Accelerate Training: Instrument and profile training runs to find bottlenecks • Engineer for the Hardware: Apply a working understanding of GPU architecture • Serve at Scale: Deploy and operate multiple models within shared GPU clusters on GKE • Drive Efficiency: Own GPU utilization as a first-class metric • Collaborate & Consult: Work directly with clients to understand performance requirements

• Design and build our central MLOps Platform covering the complete ML lifecycle • Architect robust CI/CD workflows and self-service capabilities • Partner with Data Scientists, ML Engineers, and Infrastructure teams • Establish and evangelize MLOps best practices across the organization • Optimize infrastructure costs and enhance observability