Hardware experience is a real plus, but not required — you will be willing and able to learn quicklyStrong candidates may also have experience withChip development in any form (the strongest plus): RTL/SystemVerilog, functional verification (UVM), DFT, physical design/STA, FPGA, emulation, or silicon bring‑up and validationEDA tool flows and Tcl scripting; reading waveforms, logs, and regressionsFine‑tuning or post‑training (SFT, RLHF/DPO), RAG over proprietary technical data, or multi‑agent orchestrationDeep software engineering: C++ or Rust, developer‑facing internal platforms, CI/CD at scale, or infrastructure (Docker, Slurm, Ray)Representative projectsIn your first 30 days, pick one hardware team's worst recurring pain, ship an agent for it, and prove adoption with usage dataBuild an agent that triages overnight regression failures, clusters them by root cause, and drafts bug reports with waveform and log evidence attachedWire Claude Code‑style agents into our EDA and validation flows via MCP so engineers can drive simulations, queries, and lab equipment from natural languageCreate a retrieval system over our specs, design docs, and past debug history that cuts ramp time for new engineersDesign an eval suite that measures agent performance on real verification and debug tasks, and use it to decide which workflows to automate nextPrototype AlphaEvolve‑style optimization loops that propose and automatically verify improvements to test programs or flow scriptsBenefitsFull medical, dental, and vision packages, with generous premium coverageHousing subsidy of $2,000/month for those living within walking distance of the officeDaily lunch and dinner in our officeRelocation support for those moving to San Jose (Santana Row)Unlimited compute budget subject to ROI justificationHow we're differentEtched believes in Bitter Lesson. Key responsibilitiesBuild, deploy, and maintain LLM‑agent workflows that accelerate chip development: debug triage, testbench and coverage work, log/waveform analysis, EDA script generation, and engineering knowledge retrievalEmbed with hardware teams to find the highest‑leverage pain points, then turn them into automated workflows with measurable adoptionDesign rigorous evals for agent performance on real silicon‑engineering tasks — not proxy metrics — and use them to drive iterationIntegrate agents with our internal infrastructure: simulation and emulation flows, CI/regression systems, lab equipment, and issue tracking, via tool‑calling and MCPChampion adoption: documentation, training, and fast feedback loops with the engineers who use what you buildYou may be a good fit if you haveA track record of solving hard problems across stacks and domains — you enjoy being dropped into unfamiliar territory and figuring it outComfort with Python and code: you can read it, modify it, debug it, and direct AI to write it well.