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Applied AI Engineer, Silicon Engineering
Summary by AIEngineer.careers
Applied AI Engineer, Silicon Engineering is a AI Engineer role (full-time). with Etched. in SAN JOSE, US. Compensation shown: $150K–$275K. Imported listing (source: aihiringboard.com). Apply on the employer's site (aihiringboard.com).
Imported listing (source: aihiringboard.com) · Apply on aihiringboard.com
SAN JOSE · US
$150K–$275K
Full-time
MID
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from aihiringboard.com
Etched is building hardware for frontier intelligence and is hiring an Applied AI Engineer to embed with hardware teams and build LLM-agent workflows that accelerate chip development. You will wire agents into simulators, regressions, EDA flows, and bring-up workflows, and own evals that ensure tools engineers actually rely on. This is an internal, force-multiplier role focused on applying AI to how chips are built, not inference serving or customer-facing work.
Responsibilities
- Build, deploy, and maintain LLM-agent workflows for debug triage, testbench and coverage work, log/waveform analysis, EDA script generation, and engineering knowledge retrieval
- Embed with hardware teams to identify high-leverage pain points and turn them into automated workflows with measurable adoption
- Design rigorous evals for agent performance on real silicon-engineering tasks and use them to drive iteration
- Integrate agents with internal infrastructure (simulation/emulation flows, CI/regression systems, lab equipment, issue tracking) via tool-calling and MCP
- Champion adoption through documentation, training, and fast feedback loops
Requirements
- A track record of solving hard problems across stacks and domains
- Comfort with Python and code: able to read, modify, debug, and direct AI to write it well
Etched
San Jose · US · 553+ employees
Etched is an AI hardware company developing Frontier Inference Clusters by co-designing specialized chips, racks, software, and manufacturing methods for high-throughput AI inference. It designs and manufactures hardware systems optimized for artificial-intelligence model inference workloads.
