layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

Applied AI Engineer, Silicon Engineering at Etched | LAYIQ

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

Applied AI Engineer, Silicon Engineering

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