Applied Research - Chip Design
Core
Build agents, environments, evaluations, and post-training systems for real semiconductor design and verification workflows using agentic reinforcement learning.
Role type
Applied Research Scientist (Chip Design & RL)
Builds
Interactive verification environments, verifiers, rewards, and experiments for EDA tool usage by AI agents.
Domain
Semiconductor design automation + Reinforcement Learning
Deliverable
production ML models
Required skills
Reinforcement Learning, Agent systems, Post-training systems, Python, Experimental design, Failure attribution, EDA tool familiarity
Preferred skills
Design automation, Compilers, Formal methods, ML for systems
Technologies
Python, EDA tools, Simulation, Formal verification
Responsibilities
Build interactive verification environments where agents read specs, write/repair RTL, and invoke simulation tools; Design rewards combining functional correctness with tool efficiency and PPA constraints; Train long-horizon agents to recover from design failures and generalize across blocks; Conduct experiments on credit assignment for late feedback in multi-tool workflows.
Seniority
Mid-Senior, hands-on IC
