About the position
We are now looking for a Systems Software Engineer. Do you like to think creatively and enjoy solving challenges that require innovation? If so, we may have an opportunity for you. In Our team we define and build methodologies, Software, and flows tailored to the field of Silicon device testing, Silicon debug, and Silicon failure analysis. We owe our success to our people, some of the brightest in the world, and a company culture that fosters and encourages innovation, and fuels our creativity! Our team contributes to the advancement of all the fields in which NVIDIA participate, from gaming to building groundbreaking state-of-the-art compute platforms and Artificial Intelligence, by enabling high quality Silicon defect screening to sustain all these fields. This often requires new ways of thinking in order to meet new challenges, and we pride ourselves in our ability to tackle these challenges in ways that enable our success. If you are a like-minded person who enjoys innovation and likes to solve technical challenges then we would love to hear from you!
Responsibilities
- Work with design team to understand Hardware specification for Software development
- Develop automation Software to enable efficient test pattern generation, application of these patterns on Silicon, failure analysis, and yield learning
- Enable AI applications to optimize all aspects of our software and software workflows
- Integrating advanced tools for CI/CD, code quality, and automated testing
- Solving software engineering challenges, from automating code generation and testing to optimizing developer workflows
- Work with multi-functional teams to assess and tackle problems that involve multiple areas of expertise through the company
Requirements
- Excellent communication skills
- MS or higher degree in EE or CS (or equivalent experience)
- 3+ years of strong experience in C++ Software development in fields related to Silicon testing and Automatic Test Equipment (ATE)
- 2+ years working with AI code generation, tooling, models and deep learning to develop software applications
- Practical understanding of modern AI architectures and how to apply them
- Strong python and scripting skills
- Good familiarity with gitlab and software development workflows
- Familiarity with BDD and TDD software development practices
Nice-to-haves
- Knowledge of Design for Test (DFT) including fault models, ATPG, and fault simulation
- Familiarity with creating scalable, containerized (e.g., Docker) development environments
Benefits
- Equity and benefits
