Job Description

About the Team:

The ML Inference Platform is part of the AV ML Infrastructure organization. Our team owns the cloud-agnostic, reliable, and cost-efficient platform that powers GM’s AI efforts. We’re proud to serve teams developing autonomous vehicles (L3/L4/L5), as well as other groups building AI-driven products for GM and its customers. We enable rapid innovation and feature development by optimizing for high-priority, ML-centric use cases. Our platform supports the serving of state-of-the-art (SOTA) machine learning models for experimental, online and bulk inference, with a focus on performance, availability, concurrency, and scalability. We’re committed to maximizing GPU utilization across platforms (B200, H100, A100, and more) while maintaining reliability and cost efficiency.

About the Role:

We are seeking a Senior ML Infrastructure engineer to help build and scale robust platforms for ML Inference workflows. In this role, you’ll work closely with ML engineers and researchers to ensure efficient model serving and inference in production, for workflows such as data mining, labeling, model distillation, evaluations, simulations and more. This is a high-impact opportunity to influence the future of AI infrastructure at GM. You will play a key role in shaping the architecture, roadmap and user-experience of a robust ML inference service supporting real-time, batch, and experimental inference needs. The ideal candidate brings experience in designing distributed systems for ML, strong problem-solving skills, and a product mindset focused on platform usability and reliability.

What you’ll be doing:

• Design and implement core platform backend software components.

• Collaborate with ML engineers and researchers to understand critical workflows, parse them to platform requirements, and deliver incremental value.

• Lead technical decision-making on model serving strategies, orchestration, caching, model versioning, and auto-scaling mechanisms for highly optimized use of accelerators.

• Drive the development of monitoring, observability, and metrics to ensure reliability, performance, and resource optimization of inference services.

• Proactively research and integrate state-of-the-art model serving frameworks, hardware accelerators, and distributed computing techniques.

• Lead technical initiatives across GM’s ML ecosystem.

• Raise the engineering bar through technical leadership, establishing best practices.

• Contribute to open source projects; represent GM in relevant communities.

Minimum Requirements

• 5+ years of industry experience, with focus on machine learning systems or high performance backend services.

• Expertise in either Python, C++ or other relevant coding languages.

• Expertise in ML inference, model serving frameworks (triton, rayserve, vLLM etc).

• Strong communication skills and a proven ability to drive cross-functional initiatives.

• Ability to thrive in a dynamic, multi-tasking environment with ever-evolving priorities.

Preferred Qualifications

• Deep expertise building zero-to-one ML infrastructure platforms.

• Experience working with or designing interfaces, apis and clients for ML workflows.

• Experience with Ray framework, and/or vLLM.

• Experience with distributed systems, and handling large-scale data processing.

• Familiarity with telemetry, and other feedback loops to inform product improvements.

• Familiarity with hardware acceleration (GPUs) and optimizations for inference workloads.

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 New York, Colorado, California, or Washington.

• The salary range for this role is $155,420 to $395,900. 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.

• 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.

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.

Relocation: This job may be eligible for relocation benefits.

Remote/Hybrid: This role is based remotely but if you live within a 50-mile radius of Mountain View, you are expected to report to that location three times a week, at minimum.

About GM

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Why Join Us

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Benefits Overview

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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.

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