Applied AI Engineer, Silicon Engineering

About this role

Etched seeks an Applied AI Engineer to build LLM-based agents and tooling that accelerate chip development across RTL design, verification, and silicon validation. You'll embed with hardware teams to automate high-leverage workflows—debug triage, waveform analysis, EDA scripting—and own rigorous evals that drive real adoption, not just demos.

What you'll do

• Build and deploy LLM-agent workflows for chip development tasks like debug triage, log/waveform analysis, and EDA script generation
• Partner with hardware teams to identify pain points and translate them into measurable automated solutions
• Design and run rigorous evals on real silicon-engineering tasks to validate agent performance and guide iteration
• Integrate agents with internal infrastructure: simulators, CI/regression systems, lab equipment, and issue tracking via tool-calling and MCP
• Drive adoption through documentation, training, and feedback loops with end-user engineers
• Ramp quickly on unfamiliar chip-development domains and troubleshoot cross-stack problems

What they're looking for

• Python and code debugging/modification
• Building and shipping production LLM agents with context engineering and tool integration
• Eval-driven development and performance measurement
• AI fluency for learning and ramping on new problems
• High agency and problem-finding in ambiguous settings
• Chip development knowledge (RTL, verification, DFT, or bring-up) or ability to learn quickly
• EDA tool flows and Tcl scripting
• LLM fine-tuning, RAG, or multi-agent orchestration

Likely interview questions

• Tell us about a time you shipped an LLM-based agent or AI tool that real users depended on. What was the hardest part — was it the model, the integration, or getting people to actually use it?
• Walk us through how you'd approach building an agent for a silicon engineering task you've never done before. How would you use AI to ramp, and how would you know if it actually works?