{"schemaVersion":"jobsearcher.job.v1","id":"b08feb73d47817e017ae7648","url":"https://jobsearcher.com/jobs/b08feb73d47817e017ae7648","canonicalUrl":"https://jobsearcher.com/jobs/b08feb73d47817e017ae7648","title":"Senior Software Engineer - Python Numerical Computing Libraries","description":"We are looking for an experienced software professional to contribute to design and development of accelerated and distributed implementations of Python APIs for numerical computing. In the last decade, Python has become the de-facto programming language for practitioners in AI, data science and HPC, through popular frameworks such as NumPy, SciPy, TensorFlow and PyTorch. These frameworks provide an efficient high-level programming interface, allowing their users to focus on their application while providing highly optimized implementations. NVIDIA has been at the forefront of providing GPU-accelerated implementations of the fundamental components of these frameworks.\r\nJoin our dynamic team to help develop and optimize GPU-accelerated and distributed implementations of Python numerical libraries, supporting Python-based frameworks in various ecosystems. This developer will be a crucial member of a team that is working to unlock the power of distributed GPU computing for domains such as scientific computing, data analytics, deep learning, and professional graphics, running on hardware ranging from supercomputers to the cloud!\r\nWhat you will be doing: Work closely with product management and internal or external partners, to understand use cases and requirements, and contribute to the technical roadmaps of libraries\r\nArchitect, prioritize, and develop accelerated and distributed implementations of numerical algorithms\r\nDesign future-proof Python APIs for accelerated numerical/scientific computing libraries\r\nAnalyze and improve the performance of developed APIs on various CPU and GPU architectures, especially as a part of customer-critical end-to-end workflows\r\nPrototype integrations of developed APIs into targeted frameworks\r\nWrite effective, maintainable, and well-tested code for production use\r\nContribute to the development of runtime systems that underlay the foundation of multi-GPU computing at NVIDIA\r\nWhat we need to see: BS, MS or PhD degree in Computer Science, Applied Math, Electrical Engineering or related field (or equivalent experience)\r\n6+ years of relevant industry experience or equivalent academic experience after BS\r\nExcellent Python, C++ and CUDA programming skills\r\nStrong understanding of fundamental numerical methods, dense and sparse array computing\r\nDeep familiarity with Python numerical computing libraries (e.g. NumPy, SciPy), including accelerated implementations (e.g. CuPy, Jax.NumPy, NumS, cuNumeric)\r\nExperience developing and publishing Python libraries, following standard methodologies for pythonic API design\r\nStrong background with parallel programming and performance analysis\r\nWays to stand out from the crowd: 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)\r\nExperience with low-level GPU performance optimization\r\nExperience building, debugging, profiling and optimizing distributed applications, on supercomputers or the cloud\r\nBackground with tasking or asynchronous runtimes\r\nBackground on compiler optimization techniques, and domain-specific language design\r\nYour base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.\r\nYou will also be eligible for equity and benefits.\r\nApplications for this job will be accepted at least until April 13, 2026.\r\nEqual Opportunity Statement NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.\r\nJ-18808-Ljbffr","company":"Nvidia Gruppe","rawCompany":"nvidia gruppe","city":"Santa Clara","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-08-07T00:51:16.235Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1251.00","title":"Computer Programmers","slug":"computer-programmers"}],"industries":[{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Senior Software Engineer - Python Numerical Computing Libraries","description":"We are looking for an experienced software professional to contribute to design and development of accelerated and distributed implementations of Python APIs for numerical computing. In the last decade, Python has become the de-facto programming language for practitioners in AI, data science and HPC, through popular frameworks such as NumPy, SciPy, TensorFlow and PyTorch. These frameworks provide an efficient high-level programming interface, allowing their users to focus on their application while providing highly optimized implementations. NVIDIA has been at the forefront of providing GPU-accelerated implementations of the fundamental components of these frameworks.\r\nJoin our dynamic team to help develop and optimize GPU-accelerated and distributed implementations of Python numerical libraries, supporting Python-based frameworks in various ecosystems. This developer will be a crucial member of a team that is working to unlock the power of distributed GPU computing for domains such as scientific computing, data analytics, deep learning, and professional graphics, running on hardware ranging from supercomputers to the cloud!\r\nWhat you will be doing: Work closely with product management and internal or external partners, to understand use cases and requirements, and contribute to the technical roadmaps of libraries\r\nArchitect, prioritize, and develop accelerated and distributed implementations of numerical algorithms\r\nDesign future-proof Python APIs for accelerated numerical/scientific computing libraries\r\nAnalyze and improve the performance of developed APIs on various CPU and GPU architectures, especially as a part of customer-critical end-to-end workflows\r\nPrototype integrations of developed APIs into targeted frameworks\r\nWrite effective, maintainable, and well-tested code for production use\r\nContribute to the development of runtime systems that underlay the foundation of multi-GPU computing at NVIDIA\r\nWhat we need to see: BS, MS or PhD degree in Computer Science, Applied Math, Electrical Engineering or related field (or equivalent experience)\r\n6+ years of relevant industry experience or equivalent academic experience after BS\r\nExcellent Python, C++ and CUDA programming skills\r\nStrong understanding of fundamental numerical methods, dense and sparse array computing\r\nDeep familiarity with Python numerical computing libraries (e.g. NumPy, SciPy), including accelerated implementations (e.g. CuPy, Jax.NumPy, NumS, cuNumeric)\r\nExperience developing and publishing Python libraries, following standard methodologies for pythonic API design\r\nStrong background with parallel programming and performance analysis\r\nWays to stand out from the crowd: 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)\r\nExperience with low-level GPU performance optimization\r\nExperience building, debugging, profiling and optimizing distributed applications, on supercomputers or the cloud\r\nBackground with tasking or asynchronous runtimes\r\nBackground on compiler optimization techniques, and domain-specific language design\r\nYour base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.\r\nYou will also be eligible for equity and benefits.\r\nApplications for this job will be accepted at least until April 13, 2026.\r\nEqual Opportunity Statement NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.\r\nJ-18808-Ljbffr","datePosted":"2026-08-07T00:51:16.235Z","dateModified":"2026-08-07T00:51:16.235Z","hiringOrganization":{"@type":"Organization","name":"Nvidia Gruppe","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Santa Clara","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"b08feb73d47817e017ae7648"},"url":"https://jobsearcher.com/jobs/b08feb73d47817e017ae7648"}}