Research Engineer, AI for Chip Design

Highlights

• Apply AI systems to semiconductor engineering to shorten chip design cycles.
• Build RL environments for RTL generation and physical design optimization.
• Turn successful experiments into reusable research and training workflows.

What they offer

• Equity

Ideal candidate

Independent, experimentally rigorous engineer who loves turning ideas into working software and learns fast alongside hardware specialists to build tools that improve real chip designs.

Requirements

• Strong programming and debugging skills with track record turning ideas into working software
• Experience with reinforcement learning, model evaluations, post-training, or applied ML research
• Experience building tool-using agents, reward functions, or automated evaluation systems
• Ability to form hypotheses, design experiments, and distinguish signal from noise or evaluation errors
• Work independently on ambiguous problems and make practical decisions on what to build next
• Communicate progress clearly and collaborate across research, software, and hardware
• Familiarity with experiment orchestration, distributed training, or research infrastructure
• Experience with RTL, Verilog/SystemVerilog, EDA tools, formal verification, or chip-design automation