As a software engineer you will craft and implement APIs and services to support the execution of workflows to validate the design of NVIDIA's chips. The systems we develop will operate at a large scale, running millions of tests per day in a distributed cloud computing environment with thousands of heterogeneous servers to verify multiple designs in many configurations. You will continuously innovate and develop scalable, reliable, high-performance systems, and tools to enable the next generation of chips.
What you'll be doing:
- Build microservices that are reliable, scalable, and maintainable.
- Improve the current system's ability to schedule and utilize resources, improve performance, increase reliability, and provide better throughput.
- Design interfaces that are efficient and easy to use for hundreds of engineers throughout the world.
- Join an agile and dynamic software development team with very high production quality standards.
- Participate in the full life-cycle of tool development, test, and deployment.
- Work closely with other team members and internal customers to understand their processes, requirements, and needs.
- Directly contribute to the overall quality of and improve time to market for NVIDIA's next-generation chips.
What we need to see:
- Excellent software engineering skills, including applied knowledge of OOP, design patterns, distributed systems, multiprogramming, and microservices.
- Skilled in Python and common service and/or multiprogramming-related packages.
- BS in Computer Science (or equivalent experience); MS (preferred) and 2+ years of experience.
- Excellent planning, presentation, and general communication skills.
- The flexibility and adaptability to work in an exciting environment with changing requirements.
- A passion for improving the productivity and efficiency of other engineers.
Ways to stand out from the crowd:
- Experience developing and deploying automated testing infrastructure.
- Deep understanding of distributed and microservice architecture principles such as service deployments in various contexts including HPC clusters.
- Experience with Linux ecosystems, including development and debugging tools.
- Familiarity with chip design and/or other verification workflows or expertise in Perl, C/C++, JavaScript, or TypeScript.
- Solid understanding of and experience with AI coding accelerators like Cursor or CoPilot.
