Senior DFX Software Engineer - Machine Learning

We are now looking for a Senior DFX Software Engineer - Machine Learning.

Do you like to think creatively and enjoy solving challenges that require innovation?

If so, we may have an opportunity for you.

On 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 participates, 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!

What you'll be doing:

• Develop high performance software to enable design and development of efficient test pattern generation, application of these patterns on Silicon, failure analysis, and yield learning
• Create efficient parallel graph traversal and graph analysis techniques
• Work with multi-functional teams to assess and tackle problems that involve multiple areas of expertise through the company
• Apply LLMs, RAGs, graph-based ML approaches, and reinforcement learning to define innovative solutions

What we need to see:

• BS in EE or CS (or equivalent experience); MS or higher degree preferred
• 5+ years of experience in Software development
• Strong programming experience in Python or C++. Hands on development in modern C++ is a huge plus.
• Experience use of LLMs (Large Language Models), GNNs (Graph Neural Networks), and Reinforcement Learning for efficient EDA solution.
• Expertise in high performance algorithms for DFT, simulations, and failure analysis.
• Understanding of different agent architectures, RAG systems, and communication protocols
• Deep familiarity with reinforcement learning algorithms like PPO, SAC, or Q-learning, including experience tuning hyper-parameters and reward functions
• Hands on experience with large scale training (e.g., ZeRO) and data processing (e.g. Spark)