Machine Learning Infrastructure Engineer Intern

Identify Training Bottlenecks: Profile and analyze Bird's Eye View (BEV) model training pipelines to pinpoint computational and memory bottlenecks.

Develop Custom Kernels: Design and implement high-performance custom compute kernels using CUDA, Triton, or C++ to accelerate the model training process.

Leverage LLMs for Optimization: Explore and integrate Large Language Models (LLMs) to assist in generating high-performance code and optimizing kernel logic.

Automate Profiling Workflows: Build systems to automate performance profiling and analysis using tools like NVIDIA Nsight and the PyTorch Profiler.

Iterative Performance Tuning: Continuously analyze profiling data generated by both human and LLM-assisted workflows to maximize GPU utilization and reduce training times.