Machine Learning Infrastructure Engineer Intern

Requirements

• Systems Programming:
  Strong proficiency in C++ and a solid understanding of memory management, computer architecture, and parallel processing principles
• Deep Learning Frameworks:
  Hands‑on experience with PyTorch, specifically understanding custom operations, autograd, and training loops
• Performance‑Oriented Mindset:
  Strong problem‑solving skills with a deep interest in performance tuning, algorithmic efficiency, and low‑level system optimization
• (Desirable) GPU Programming
  
  Experience:
  
  Practical experience writing and optimizing custom GPU kernels using CUDA or OpenAI Triton
• (Desirable) Hardware Profiling Tools:
  Familiarity with hardware and software profiling tools, particularly NVIDIA Nsight (Systems/Compute) and the PyTorch Profiler
• (Desirable) LLM for Code Generation:
  Experience using or prompting LLMs for code writing, refactoring, or exploring AI‑assisted software development workflows
• (Desirable) Autonomous Vehicle Perception: A foundational understanding of Bird’s Eye View (BEV) models, 3D perception, or spatial transformers used in autonomous driving

• Ready to get hands‑on with real‑world, large‑scale data challenges? We’re seeking a Machine Learning Infrastructure Engineer Intern to join us in a project that focuses on identifying the bottlenecks and implementing high‑performance custom kernels (using CUDA, Triton, or C++) to accelerate BEV model training
• Uniquely, this internship will also explore the utilization of LLMs (Large Language Models) to assist in high‑performance code generation, kernel optimization, and automated performance profiling with Nsight and Pytorch profiler
• 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