NVIDIA is looking to develop and productize deep learning solutions in autonomous driving vehicles by developing compiler technology to optimize deep learning models for their unique hardware architecture.
Requirements
- MS or PhD degree in computer science, computer vision, robotics, computer architecture or equivalent experience in technical field (or equivalent experience)
- 5+ years of work experience in software development.
- 2+ years of experience in developing deep learning frameworks (e.g. PyTorch, JAX, TensorFlow, ONNX, etc.) or compiler technologies (e.g. LLVM, MLIR, TVM, Triton, etc.).
- Domain experience in technologies used for GPU programming (e.g. CUDA C++ and/or DSLs like OpenAI Triton) or with system-level optimization for deep learning training or inference.
- Strong C/C++ programming skills
- Familiar with start-of-the-art deep learning techniques for inference and training.
Responsibilities
- Developing compiler technologies to accelerate deep learning inference on NVIDIA hardware platforms for Physical AI.
- Working across a wide range of abstractions from model fine-tuning and quantization to low-level kernel development and performance optimization.
- Develop workflows that let users leverage frameworks (e.g. PyTorch, JAX) and compiler technologies tools (e.g. MLIR, Triton) without forgoing performance
- Work with customers to help accelerate their workloads on NVIDIA platforms.
- Stay up to date with the latest research and innovations in deep learning, implement and experiment with new insights to improve NVIDIA's Physical AI DNNs.
Other
- MS or PhD degree in computer science, computer vision, robotics, computer architecture or equivalent experience in technical field (or equivalent experience)
- 5+ years of work experience in software development.
- Willing to take action and have strong analytical skills.
- Open source project ownership or contribution, healthy GitHub repositories, guiding and/or mentoring experience
- Travel requirements not mentioned
