Job Opportunities API

About Etched Etched is building hardware for frontier intelligence. We co-design chips, racks, software, and manufacturing to deliver best-in-class throughput and latency across both prefill and decode workloads. Our first products are heavily focused on inference. Backed by hundreds of millions from top-tier investors and staffed by leading engineers, Etched is redefining the infrastructure layer for the fastest growing industry in history. Job Summary We are using AI to build AI chips. AI agents are starting to genuinely work for verification, debug, and EDA flows — we want someone to bring that inside Etched and push past it. As an Applied AI Engineer, you will embed with our hardware teams — RTL design, verification, DFT, physical design, and silicon validation — and build the agents and tooling that multiply their output. You'll wire LLM agents into simulators, regressions, waveform and log analysis, EDA flows, and bring-up workflows, and own the evals that separate demos from tools engineers actually rely on. This is an internal, force-multiplier role: your success is measured by how much faster the chip team moves, not by lines of code you ship yourself. It is not a customer-facing role and not about inference serving — it's AI applied to how we build the chip itself. You do not need to be a chip designer or a traditional software engineer — you need to be an exceptional problem solver who has shipped real agentic systems, works comfortably across stacks and domains, and uses AI to ramp on hard new problems fast. 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 a