Senior ML Accelerator Engineer - GPU

General Motors; San Francisco (CA); On-site; USD 128,700 - 261,300; Full time

Benefits offered by this job

Health and wellness benefits

Paid vacation and holidays

Tuition assistance programs

GM vehicle discounts

Job summary

General Motors is looking for an experienced GPU Kernel Developer to join their AI Kernels team in San Francisco. The successful candidate will design and implement CUDA-based kernels to enhance performance on automated driving systems.

This role requires strong programming skills, rigorous problem-solving abilities, and experience in optimizing performance. GM offers competitive compensation and a range of health and wellbeing benefits.

Qualifications

• 3+ years of relevant industry experience or equivalent.
• Excellent GPU programming skills in CUDA and understanding of GPU architecture.
• Hands-on experience with performance optimization using NSight or similar tools.

Responsibilities

• Design and implement CUDA-based kernels for vehicle inference workloads.
• Collaborate with cross-functional teams to deliver high-performance libraries.
• Maintain technology standards and guidelines for GPU kernel development.

Skills

GPU programming in CUDA

C++ programming

Performance benchmarking and profiling

Software architecture

Communication skills

Education

BS, MS, or PhD in Computer Science or related technical field

Tools

NSight suite

CUDA

Job description

Job Overview

GM’s vision of Zero Crashes, Zero Emissions, and Zero Congestion guides everything we do in autonomous and assisted driving. The AV organization is developing advanced automated driving technologies, including Level 4?capable fully self-driving systems, to move us toward safer, more sustainable, and more accessible mobility. For the AI Kernels & Compilers team, that mission shows up in the details: turning cutting?edge perception, prediction, and planning research into production?grade software that can run efficiently and reliably on real vehicles at scale. We pioneer new approaches to model export, kernel development, and performance engineering so that each cycle on our accelerators translates into better situational awareness, faster reaction times, and more robust behavior on the road.

Team Overview

The AI Kernels team builds high?performance GPU kernels and custom libraries that sit at the heart of our on?vehicle ML inference for ADAS and autonomous driving. We make core AI workloads faster, more reliable, and easier to maintain and deploy on real cars under real?world constraints.

Responsibilities

• Design, implement, benchmark, and iterate on CUDA-based kernels and custom operators to squeeze every last drop of performance out of on‑vehicle inference workloads.
• Build and improve tooling and infrastructure that make it easier to profile, debug, and validate CUDA kernels and accelerator–backend code across the AV stack.
• Partner with AI Solutions, Compilers, and Architecture to translate model and system requirements into concrete kernel roadmaps, priorities, and project plans.
• Collaborate with cross‑functional teams (compiler, performance tooling, runtime, deployment solutions) to deliver reusable, reliable, high‑performance libraries into production.
• Maintain high‑technology standards, methodologies, processes, and guidelines for GPU kernel development and performance engineering through code review.
• Manage relationships with internal customers to ensure our kernels and libraries meet real‑world needs.

Required Qualifications

• Minimum 3+ years of relevant industry experience or equivalent.
• BS, MS, or PhD in CS or related technical field.
• Excellent GPU programming skills in CUDA with a thorough understanding of parallel programming patterns and GPU architecture.
• Hands‑on experience benchmarking, profiling, debugging, and optimizing accelerator libraries and kernels to extract optimal performance using the NSight suite of tools or similar.
• Strong background in software architecture, library design, and design patterns.
• Strong C++ programming skills with the ability to feel comfortable in large codebases.
• Solid background in system performance, high‑performance computing, and/or architecture‑aware optimizations.
• Strong communication skills and the ability to work collaboratively within a team.
• Excellent analytical and problem‑solving skills.

Preferred Qualifications

• Experience with tensor core programming, CUTLASS and/or CuTe.
• Experience with ML model architectures, particularly transformer‑based.
• Experience with low‑latency or real‑time systems.
• Experience with lower levels of an accelerator software stack (i.e., drivers, runtimes, and compilers).

Compensation

• The salary range for this role: $128,700 to $261,300. 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.

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.

EEO Statement

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.