Senior Research Scientist, Electronic Design Automation

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

NVIDIA is seeking a Senior Research Scientist to conduct research at the intersection of AI, GPU computing, and Electronic Design Automation (EDA). The role involves defining and conducting original research in EDA algorithms, VLSI design methodology, and advanced machine learning techniques, with a focus on applying deep learning and GPU acceleration to improve chip design tools and flows. The scientist will collaborate with internal teams and the research community, publishing findings and potentially translating research into products.

What you'd actually do

  1. Define and conduct original research across EDA algorithms, VLSI design methodology, and advanced machine learning techniques.
  2. Innovate in EDA software and algorithms, with applications spanning supervised, unsupervised, and reinforcement learning, 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. Engage with the global research community by publishing in premier conferences, presenting at leading venues, and driving thought leadership.

Skills

Required

  • PhD in Computer Science, Electrical/Computer Engineering, or related field (or equivalent experience)
  • 3+ years of post-PhD research experience
  • Deep knowledge in EDA/VLSI (e.g., synthesis, physical design, verification, timing, reliability, or CAD algorithms)
  • 5+ years applying machine learning/deep learning (supervised, unsupervised, RL)
  • 5+ years of experience in software development with proficiency in at least two of Python, PyTorch, C++, or CUDA
  • strong expertise in high-performance computing, parallelism, optimization, and large-scale software engineering
  • experience in leading research projects or teams
  • mentoring junior scientists
  • cross-functional collaboration
  • collaboration with academia or industry
  • Excellent written and verbal communication skills
  • presenting at top conferences
  • releasing high-impact open-source tools, datasets, or frameworks

Nice to have

  • GPU/accelerator-based computing to large-scale problems

What the JD emphasized

  • strong record of impactful publications in top EDA and AI/ML venues
  • proven ability to set research direction and translate ideas into tools or products
  • Deep knowledge in EDA/VLSI
  • 5+ years applying machine learning/deep learning (supervised, unsupervised, RL)
  • 5+ years of experience in software development with proficiency in at least two of Python, PyTorch, C++, or CUDA
  • strong expertise in high-performance computing, parallelism, optimization, and large-scale software engineering
  • experience in leading research projects or teams
  • track record of mentoring junior scientists
  • driving cross-functional initiatives with circuits/VLSI/architecture groups
  • fostering collaborations with academia or industry
  • demonstrated experience presenting at top conferences
  • track record of releasing high-impact open-source tools, datasets, or frameworks

Other signals

  • applying machine learning/deep learning (supervised, unsupervised, and reinforcement learning)
  • GPU-accelerated optimization methods
  • deep learning and GPU computing to improve ASIC and VLSI design tool flows