AI & EDA Research Scientist (PhD) — GPU-Driven Design

NVIDIA Corporation; United States; On-site; USD 168,000 - 265,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA Research is seeking a world-class PhD researcher to drive groundbreaking research at the intersection of AI, GPU computing, and EDA. You will define and conduct original research across EDA algorithms, VLSI design methodology, and advanced AI techniques.

You will collaborate with circuit design, VLSI, and architecture teams, publish and present your research at conferences, and work on GPU-accelerated optimization methods.

Qualifications

• PhD in Computer Science, Electrical/Computer Engineering, or related field.
• Strong programming and systems skills in Python, PyTorch, C++, or CUDA.
• Publications in top EDA or AI/ML venues.
• Strong collaboration, self-motivation and communication skills.

Responsibilities

• Define and conduct original research across EDA, VLSI design methodology, and advanced AI techniques.
• Innovate in EDA software and algorithms, with applications spanning supervised, unsupervised, reinforcement learning, agentic AI systems, as well as GPU-accelerated optimization methods.
• Apply deep learning and GPU computing to improve ASIC and VLSI design tool flows.
• Collaborate cross-functionally with circuit design, VLSI, and architecture teams, ensuring research translates into real-world product impact.
• Publish and present your original research, speak at conferences and events
• Collaborate with external researchers and a diverse set of internal product teams.

Skills

Python

PyTorch

C++

CUDA

Education

PhD in Computer Science, Electrical/Computer Engineering, or related field

Job description

NVIDIA Research is seeking a world-class PhD researcher to drive groundbreaking research at the intersection of AI, GPU computing, and EDA. You will define and conduct original research across EDA algorithms, VLSI design methodology, and advanced AI techniques.

You will collaborate with circuit design, VLSI, and architecture teams, publish and present your research at conferences, and work on GPU-accelerated optimization methods.