Research Scientist, Electronic Design Automation - New College Grad 2026

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Research Scientist role focused on applying AI/ML techniques, including supervised, unsupervised, reinforcement learning, and agentic AI, to Electronic Design Automation (EDA) and VLSI design. The role involves defining and conducting original research, innovating in EDA software and algorithms, and applying deep learning to improve chip design tools, with a strong emphasis on publication and collaboration.

What you'd actually do

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

Skills

Required

  • PhD in Computer Science, Electrical/Computer Engineering, or related field (or equivalent experience)
  • Proficiency in at least two of Python, PyTorch, C++, or CUDA
  • Publications in top EDA and AI/ML venues
  • Expertise in EDA algorithms
  • Experience applying machine learning/deep learning (supervised, unsupervised, RL, agentic AI) to impactful problems
  • Excellent self-motivation
  • High degree of creativity
  • Passion for research
  • Collaboration skills
  • Ability to work effectively within a research team
  • Excellent written and verbal communication skills
  • Proven experience communicating technical work

Nice to have

  • GPU computing
  • Agentic AI systems

What the JD emphasized

  • Publications in top EDA and AI/ML venues
  • Expertise in EDA (e.g., synthesis, physical design, design verification, timing) algorithms combined with publications and project experience applying machine learning/deep learning (supervised, unsupervised, RL, agentic AI) to impactful problems.

Other signals

  • AI/ML for EDA
  • GPU acceleration
  • Deep learning research