NVIDIA is looking to develop and optimize GPU-accelerated and distributed implementations of Python numerical libraries to unlock the power of distributed GPU computing for domains such as scientific computing, data analytics, deep learning, and professional graphics
Requirements
- Excellent Python, C++ and CUDA programming skills
- Strong understanding of fundamental numerical methods, dense and sparse array computing
- Deep familiarity with Python numerical computing libraries (e.g. NumPy, SciPy), including accelerated implementations (e.g. CuPy, Jax.NumPy, NumS, cuNumeric)
- Experience developing and publishing Python libraries, following standard methodologies for pythonic API design
- Strong background with parallel programming and performance analysis
- Experience using/contributing to Python libraries for data science (e.g. Pandas), machine learning (e.g. scikit-learn) and deep learning (e.g. TensorFlow, PyTorch)
- Experience with low-level GPU performance optimization
Responsibilities
- Work closely with product management and internal or external partners, to understand use cases and requirements, and contribute to the technical roadmaps of libraries
- Architect, prioritize, and develop accelerated and distributed implementations of numerical algorithms
- Design future-proof Python APIs for accelerated numerical/scientific computing libraries
- Analyze and improve the performance of developed APIs on various CPU and GPU architectures, especially as a part of customer-critical end-to-end workflows
- Prototype integrations of developed APIs into targeted frameworks
- Write effective, maintainable, and well-tested code for production use
- Contribute to the development of runtime systems that underlay the foundation of multi-GPU computing at NVIDIA
Other
- BS, MS or PhD degree in Computer Science, Applied Math, Electrical Engineering or related field (or equivalent experience)
- 6+ years of relevant industry experience or equivalent academic experience after BS
- Travel requirements not specified
- Visa requirements not specified
- Must be eligible to work in the country of employment
