ApplySarthi

Senior Systems Software Engineer, CUDA Driver

Nvidia

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Questions you are likely to be asked

  1. Why do you want to join Nvidia?
  2. What is your experience with Deep learning? Tell me one thing you learned the hard way.
  3. How do you decide what to test, and what does good code review look like to you?
  4. Describe a time a deadline forced a trade-off in quality. What did you choose and why?
  5. How would you design an API for a feature you have worked on?

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NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. We're looking to grow our company and form teams with the most inquisitive people in the world. Join us at the forefront of technological advancement. We are hiring software engineers to work on the CUDA driver for Tegra. CUDA is NVIDIA’s platform for accelerating general purpose computation on the GPU. Our team delivers features and improvements to better realize the potential of NVIDIA hardware for a growing range of computational workloads, ranging from deep learning, scientific computation, and self-driving cars to video games and virtual reality. CUDA defines a unified programming model across a range of system configurations and hardware capabilities. To accomplish this, the CUDA driver interacts with GPU hardware, kernel mode drivers, user mode drivers, and the operating system. What you’ll be doing: As a member of our team, you will use your design abilities, coding expertise, and creativity to deliver the best compute platform in the world. You will craft elegant solutions to exciting problems and shape the future direction of CUDA as you collaborate with your peers across NVIDIA. You will architect and implement new features Coordinate development efforts across multiple teams Help define forward-looking improvement to the CUDA APIs and programming model Write effective, maintainable, and well-tested code Focus on developing code that targets the Tegra devices Integrate AI practices into the development workflows across the compute organisation What we need to see: BS or MS degree in Computer Science, Electrical Engineering or related field (or equivalent experience) 8+ years of relevant industry experience or equivalent academic experience after BS Strong C and C++ programming skills Experience working with large codebases Experience with operating system interfaces for threads, process control, and virtual memory Experience writing and debugging multithreaded programs Understanding of system level architecture, such as interconnects, memory hierarchy, interrupts, and memory-mapped IO Strong interest in system software Good written communication Ways to stand out from the crowd: Prior experience with parallel computing - preferably writing CUDA Programs or Libraries that use CUDA User mode driver development experience Experience with Linux Systems Software development as well as experience maintaining and extending programming models or higher-level language support for similar environments

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Listed on workday · posted 2026-10-12. ApplySarthi collects openings and links to application pages; the role is advertised by Nvidia, not by us.