Senior Software Engineer - Python Numerical Computing Libraries

NVIDIA is seeking an experienced software professional to develop accelerated and distributed implementations of Python APIs for numerical computing. The role focuses on designing Python numerical libraries, optimizing performance across CPU and GPU architectures, integrating APIs with major frameworks, and contributing to multi-GPU runtime systems.

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

Requirements:

• 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
• 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
• Experience building, debugging, profiling and optimizing distributed applications, on supercomputers or the cloud
• Background with tasking or asynchronous runtimes
• Background on compiler optimization techniques, and domain-specific language design