• 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