Job Description
About the team
We focus on developing high-performance GPU kernels and custom libraries that power state-of-the-art ML models' on-vehicle inference. Our charter is to make core AI workloads faster, more reliable, and easier to maintain and deploy. That includes building custom operators when vendor libraries fall short, integrating those kernels into our ML runtime stack, and consulting on performance and CUDA debugging across the AV software stack. We collaborate closely with AI Solutions, AI Compilers, AI Architecture, and AI Tooling to ensure models can be deployed efficiently to the car while meeting strict latency and reliability targets.
About the role
As an AI Kernels intern, you'll work alongside experienced kernel, compiler, and performance engineers on real production problems-profiling GPU workloads, experimenting with new kernel implementations, and strengthening the performance and robustness of the AI stack behind GM's next-generation autonomous and assisted driving features. You'll design, implement, and benchmark CUDA kernels and supporting infrastructure, contributing to the GPU kernels and custom libraries that improve performance, reliability, and developer experience across our ML stack.
What You'll Do:
Design andoptimizeGPU kernels and supporting librariesfor core model operations used in on-vehicle inference.
Build and improve tooling and infrastructurethat make it easier to profile, debug, andvalidateCUDA kernels and accelerator-backed code.
Help define and refine kernel requirements and prioritiesby working with partners in AI Solutions, Compilers, and Architecture, and turning them into concrete tasks and project plans.
Implement, benchmark, and iterate on CUDA-based solutionsto get the most out of modern GPU hardware for real production workloads.
Take on team-specific projects, which may include performance investigations, reliability improvements, or proto@type explorations depending on current priorities.
Required Qualifications
Currently enrolled in a PhD program in Computer Science, Computer Engineering, Electrical Engineering, Applied Math / ComputationalScienceora relatedSTEM field.
Availability to work full-time (40 hoursper week) during the internship period.
Demonstrated coursework, research, or projects in GPU programming, parallel computing, high-performance computing (HPC), machine learning systems, or computer architecture.
Strong programming skills in C+ Preferred Qualifications
Experience with CUDA/CUTLASS/CuTeor other accelerator programming framework, such as OpenCL.
Familiarity withGPU performance profiling tools(e.g., Nsight Systems, Nsight Compute,nvprof).
Experience withmixed-precision computation(FP16 / INT8) and performance-accuracy tradeoffs.
Knowledge ofGPU-accelerated libraries(e.g.,cub,cuBLAS,cuDNN,TensorRT) and when to use custom kernels vs. library calls.
Background inparallel algorithms, numerical methods, or high-performance computing (HPC).
Priorresearch, publications, or coursework involving GPU acceleration or systems-level optimization.
Location:
Sunnyvale, CA
Work Arrangement:?
Hybrid: This internship is categorized as hybrid. The selected intern is expected to report to the office up to three times per week or as determined by the team.??
Compensation:
The monthly salary range for this role is $11,100 - $13,100
GM will provide a one-time lump sum taxable stipend payment to eligible students selected for the 2026 Student Program.
To help facilitate administration of the relocation stipend if you are selected, please apply using the permanent address you would move from.
What you'll get from us (Benefits):
Paid US GM Holidays
GM Family First Vehicle Discount Program
Result-based potential for growth within GM
Intern events to network with company leaders and peers
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.
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Benefits Overview
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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 (https://search-careers.gm.com/en/how-we-hire) .
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Our Culture
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