Senior DFX Software Engineer - Machine Learning

NVIDIA•Santa Clara, CA

9d•$152,000 - $287,500

About The Position

NVIDIA is seeking a Senior DFX Software Engineer focused on machine learning to develop methodologies, software, and flows for silicon device testing, silicon debug, and silicon failure analysis. The team enables high-quality silicon defect screening across NVIDIA's products and computing platforms. The role involves developing high-performance software for test-pattern generation and application on silicon, failure analysis, and yield learning. It also applies machine-learning approaches—including LLMs, retrieval-augmented generation, graph-based methods, and reinforcement learning—to address engineering challenges. The engineer will collaborate with multifunctional teams across the company.

Requirements

Bachelor's degree in Electrical Engineering or Computer Science, or equivalent experience.
At least 5 years of software development experience.
Strong programming experience in Python or C++; hands-on modern C++ development is a significant plus.
Experience using LLMs, graph neural networks, and reinforcement learning for efficient EDA solutions.
Expertise in high-performance algorithms for DFT, simulations, and failure analysis.
Understanding of agent architectures, RAG systems, and communication protocols.
Familiarity with reinforcement learning algorithms such as PPO, SAC, or Q-learning, including tuning hyperparameters and reward functions.
Hands-on experience with large-scale training, such as ZeRO, and data processing, such as Spark.
Excellent communication skills.

Nice To Haves

Master's degree or higher.
Proven deployment of large-scale agentic applications with high concurrency and agility.
Experience with software and hardware, especially involving DFT, failure analysis, and CAD tools.
Working experience with agentic models or frameworks, observability, and evaluation tools.
In-depth understanding of graph neural networks and reinforcement learning for logic design automation.
Experience fine-tuning large language models and building advanced multi-agent systems, RAG pipelines, and vector databases.

Responsibilities

Develop high-performance software for efficient test-pattern generation, application of patterns on silicon, failure analysis, and yield learning.
Create efficient parallel graph traversal and graph analysis techniques.
Work with multifunctional teams to assess and solve problems involving multiple areas of expertise.
Apply LLMs, RAGs, graph-based machine learning approaches, and reinforcement learning to define innovative solutions.