Inference Optimization Intern – Performance Modeling

United States·SunnyvaleIntern | Fallentry

OtherIntern

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Key Responsibilities

Develop analytical performance models for GPU kernels and inference workloads. Build and validate a simulator to estimate theoretical hardware performance limits.

Technical Tools

OtherIntern

About the Institute of Foundation Models

The Institute of Foundation Models is dedicated to advancing the science and engineering of large-scale AI systems. Our researchers and engineers develop cutting-edge foundation models while pushing the limits of high-performance computing and efficient AI inference. By combining deep expertise in machine learning, systems engineering, and hardware optimization, we build scalable AI solutions that drive scientific discovery and real-world impact.

As part of the team, interns work alongside world-class researchers and performance engineers to optimize the execution of large-scale foundation models on next-generation NVIDIA GPU architectures. This internship provides hands-on experience in low-level GPU performance analysis, kernel optimization, and hardware-aware inference acceleration.

This intensive internship offers a unique opportunity to contribute to the development of a simulator and profiling framework for foundation model inference on NVidia GPUs.

Responsibilities include:

Develop analytical performance models for GPU kernels and inference workloads.

Build and validate a simulator to estimate theoretical hardware performance limits.

Compare measured kernel performance against architectural peak throughput.

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.

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.

Knowledge of PTX, SASS, and low-level GPU execution.

Experience optimizing CUDA kernels for throughput and latency.

Understanding of roofline analysis, performance modeling, and hardware utilization metrics.

Experience with deep learning frameworks such as PyTorch or TensorFlow.

Strong programming skills in C++, CUDA, and Python.

Performance engineering mindset.

Strong analytical and debugging abilities.

Interest in AI systems, inference optimization, and hardware-software co-design.

Ability to work independently on research and engineering challenges.