Machine Learning Infrastructure Engineer Intern, PlusAI

• 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

• 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