Original listing text, shown exactly as published by the company.
Key responsibilities
- Build, 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 retrieval
- Embed with hardware teams to find the highest-leverage pain points, then turn them into automated workflows with measurable adoption
- Design rigorous evals for agent performance on real silicon-engineering tasks — not proxy metrics — and use them to drive iteration
- Integrate agents with our internal infrastructure: simulation and emulation flows, CI/regression systems, lab equipment, and issue tracking, via tool-calling and MCP
- Champion adoption: documentation, training, and fast feedback loops with the engineers who use what you build
You may be a good fit if you have
- A track record of solving hard problems across stacks and domains — you enjoy being dropped into unfamiliar territory and figuring it out
- Comfort with Python and code: you can read it, modify it, debug it, and direct AI to write it well. We do not care whether you write code from scratch — we care whether you ship things that work
- Fluency using AI to learn and ramp on new problems — agentic coding tools, deep research, and frontier models are how you work, not an add-on
- Hands-on experience building and shipping LLM-based agents or AI tooling that real users depend on (beyond calling an API — context engineering, tool integration, orchestration, failure analysis)
- An eval-driven mindset: you measure whether AI systems actually work before scaling them
- High agency and comfort with ambiguity — you can find the problem, not just solve the stated one
- Interest in chip development and the ability to ramp quickly on a deeply technical domain. Hardware experience is a real plus, but not required — you will be willing and able to learn quickly
Strong candidates may also have experience with
- Chip development in any form (the strongest plus): RTL/SystemVerilog, functional verification (UVM), DFT, physical design/STA, FPGA, emulation, or silicon bring-up and validation
- EDA tool flows and Tcl scripting; reading waveforms, logs, and regressions
- Fine-tuning or post-training (SFT, RLHF/DPO), RAG over proprietary technical data, or multi-agent orchestration
- Deep software engineering: C++ or Rust, developer-facing internal platforms, CI/CD at scale, or infrastructure (Docker, Slurm, Ray)
Representative projects
- In your first 30 days, pick one hardware team's worst recurring pain, ship an agent for it, and prove adoption with usage data
- Build an agent that triages overnight regression failures, clusters them by root cause, and drafts bug reports with waveform and log evidence attached
- Wire Claude Code-style agents into our EDA and validation flows via MCP so engineers can drive simulations, queries, and lab equipment from natural language
- Create a retrieval system over our specs, design docs, and past debug history that cuts ramp time for new engineers
- Design an eval suite that measures agent performance on real verification and debug tasks, and use it to decide which workflows to automate next
- Prototype AlphaEvolve-style optimization loops that propose and automatically verify improvements to test programs or flow scripts
