Senior DFX Software Engineer - Machine Learning
$152,000–$241,500 year
On-siteSanta Clara, California, United States
Santa Clara, California, United StatesOn-siteFull Time$152,000–$241,500 yearSenior LevelEnterprise
Full TimeSenior LevelEnterprise
Job Summary
Develop high-performance software for efficient test pattern generation, silicon application, and yield learning using parallel graph traversal and analysis techniques. Apply Large Language Models, RAGs, graph-based ML approaches, and reinforcement learning to define innovative solutions for silicon defect screening. Collaborate with multi-functional teams to assess complex problems spanning multiple expertise areas. This role supports NVIDIA's advancement across gaming, compute platforms, and AI by enabling high-quality silicon defect screening.
Required Qualifications
- BS in EE or CS (or equivalent experience)
- 5+ years of experience in Software development
- Strong programming experience in Python or C++
- Expertise in high performance algorithms for DFT, simulations, and failure analysis
- 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)
- Excellent communication skills
Desired Qualifications
- MS or higher degree preferred
- 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
- Understanding of different agent architectures, RAG systems, and communication protocols
- Proven deployment of large-scale agentic application with high concurrency and agility
- Experience with software and hardware especially involving DFT, failure analysis, and CAD tools
- Working experience of agentic models / frameworks, observability and evaluation tools
- In-depth understanding of the graph neural networks, and reinforcement learning for logic design automation
- Experience with fine-tuning large language models, building advanced multi-agent systems, RAG pipelines and vector databases
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