2026 Summer Intern - Machine Learning… at General Motors · Sunnyvale

Summary: General Motors is focused on developing high-performance GPU kernels and custom libraries for machine learning models. As an AI Kernels intern, you will work with experienced engineers to design, implement, and benchmark CUDA kernels and contribute to the performance and robustness of GM's AI stack for autonomous driving features. Responsibilities: - Design and optimize GPU kernels and supporting libraries for core model operations used in on-vehicle inference - Build and improve tooling and infrastructure that make it easier to profile, debug, and validate CUDA kernels and accelerator-backed code - Help define and refine kernel requirements and priorities by 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 solutions to 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 prototype explorations depending on current priorities Required Qualifications: - Currently enrolled in a PhD program in Computer Science, Computer Engineering, Electrical Engineering, Applied Math / Computational Science or a related STEM field - Availability to work full-time (40 hours per 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/CuTe or other accelerator programming framework, such as OpenCL - Familiarity with GPU performance profiling tools (e.g., Nsight Systems, Nsight Compute, nvprof) - Experience with mixed-precision computation (FP16 / INT8) and performance–accuracy tradeoffs - Knowledge of GPU-accelerated libraries (e.g., cub, cuBLAS, cuDNN, TensorRT) and when to use custom kernels vs. library calls - Background in parallel algorithms, numerical methods, or high-performance computing (HPC) - Prior research, publications, or coursework involving GPU acceleration or systems-level optimization Required Skills: GPU programming, C++, CUDA Important Skills: high-performance computing, machine learning systems, parallel computing Nice-to-Have Skills: performance profiling tools, mixed-precision computation, GPU-accelerated libraries, parallel algorithms Internship Start Date: Start in 2026 Summer 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