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PlusAI

Posted 20 days ago

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Machine Learning Infrastructure Engineer Intern

Santa ClaraOn-siteInternship

AI Summary

Machine Learning Infrastructure Engineer Intern focuses on profiling BEV model training pipelines, building high-performance compute kernels with CUDA/Trition/C++, and using LLMs to assist in code generation and profiling workflows.

About this role

PlusAI is a Physical AI company pioneering AI-based virtual driver software for factory-built autonomous trucks. Headquartered in Silicon Valley with operations in the United States and Europe, Plus was named by Fast Company as one of the World’s Most Innovative Companies. Partners including TRATON GROUP’s Scania, MAN, and International brands, Hyundai Motor Company, Iveco Group, Bosch, and DSV are working with Plus to accelerate the deployment of next-generation autonomous trucks. If you’re ready to make a huge impact and drive the future of autonomy, Plus is looking for talented individuals to join its fast-growing teams.

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

Responsibilities:

  • 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.

    • Required Skills:

  • 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.

    • Preferred Skills:

  • GPU Programming Experience: Practical experience writing and optimizing custom GPU kernels using CUDA or OpenAI Triton.
  • Hardware Profiling Tools: Familiarity with hardware and software profiling tools, particularly NVIDIA Nsight (Systems/Compute) and the PyTorch Profiler.
  • LLM for Code Generation: Experience using or prompting LLMs for code writing, refactoring, or exploring AI-assisted software development workflows.
  • Autonomous Vehicle Perception: A foundational understanding of Bird's Eye View (BEV) models, 3D perception, or spatial transformers used in autonomous driving.

    • Skills

    3D PerceptionAutogradBEV ModelsC++Code Generation With LLMsComputer ArchitectureCUDAGPU Kernel/parallel ProgrammingKernel OptimizationLow-level System OptimizationMemory ManagementNVIDIA NsightPerformance TuningProfiling ToolsPyTorchPyTorch ProfilerSpatial TransformersTraining LoopsTriton

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