Applied AI Engineer, Silicon Engineering

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

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)

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

Etched believes in the Bitter Lesson . We think most of the progress in the AI field has come from using more FLOPs to train and run models, and the best way to get more FLOPs is to build model-specific hardware. Larger and larger training runs encourage companies to consolidate around fewer model architectures, which creates a market for single-model ASICs.

We are a fully in-person team in San Jose (Santana Row), and greatly value engineering skills. We do not have boundaries between engineering and research, and we expect all of our technical staff to contribute to both as needed.