About the role#
The Institute of Foundation Models is seeking an intern to work on high-performance computing and efficient AI inference. You will work alongside researchers and engineers to optimize the execution of large-scale foundation models on next-generation NVIDIA GPU architectures. This role focuses on low-level GPU performance analysis, kernel optimization, and hardware-aware inference acceleration.
What you'll do#
- Develop analytical performance models for GPU kernels and inference workloads.
- Build and validate a simulator to estimate theoretical hardware performance limits.
- Identify performance bottlenecks in compute, memory, communication, and scheduling.
- Analyze GPU execution using NVIDIA Nsight Systems and Nsight Compute.
- Investigate PTX and SASS code generation to understand low-level execution behavior.
- Collaborate with researchers and engineers to optimize inference kernels for transformer-based models.
- Evaluate utilization of Tensor Cores, memory bandwidth, caches, and instruction pipelines.
- Design profiling methodologies for Hopper and Blackwell architectures.
- Document findings and provide actionable recommendations for performance improvements.
What you'll need#
- Currently pursuing a degree in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, High-Performance Computing, or a related quantitative discipline.
- Experience with CUDA programming and GPU kernel development.
- Understanding of NVIDIA GPU architecture and memory hierarchy.
- Familiarity with performance profiling tools such as Nsight Systems and Nsight Compute.
- Experience with deep learning frameworks such as PyTorch or TensorFlow.
- Proficiency in C++, CUDA, and Python.
- Knowledge of PTX, SASS, roofline analysis, and hardware utilization metrics.
Location & details#
- Location: Sunnyvale, California
- Term: Fall 2026
- Modality: On-site
- Status: Full-time, paid internship
