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Salary: USD 150,000 - 400,000 / annual
Etched is building hardware for frontier AI inference. The Internal Acceleration team's mandate is to build AI agents that accelerate the production of frontier tokens and transform how hardware is built in an AI-native world.
In this role, you'll work directly with founders and embed with teams building kernels, ASICs, hardware, production, and special projects. You'll build agentic harnesses and operating systems that measurably speed up Etched's engineering velocity. Success is measured by how much faster the company moves, not by lines of code written.
Key responsibilities include:
- Embedding with kernels, ASIC, hardware, and production teams to identify high-leverage bottlenecks and own solutions end-to-end, from conception to daily-use tools.
- Building and deploying LLM agents integrated into engineering workflows via tool-calling and MCP (Model Context Protocol).
- Shipping full-stack internal products from zero to one: interfaces, data pipelines, and infrastructure that define how Etched builds.
- Designing evals and tracking adoption to double down on high-impact tools and eliminate low-value ones.
- Taking on special projects directly with founders to surface cross-company problems and hit critical milestones.
This is a high-ambiguity role for engineers who thrive on ownership, have strong product sense, and want to solve time-sensitive, business-critical problems. The role is not a 9-to-5; the ideal candidate is excited about intensity and impact.
Etched is fully in-person at Santana Row in San Jose and values engineering excellence across disciplines. The company co-designs chips, racks, software, and manufacturing to deliver best-in-class throughput and latency for AI inference, backed by hundreds of millions from top-tier investors.
REQUIREMENTS:
- 1–5+ years of experience building and shipping software end-to-end, ideally at a fast-moving startup.
- Strong full-stack skills; ability to ship working solutions across the stack.
- Experience building LLM agents or AI tooling that real users depend on (beyond API calls): context engineering, tool integration, evals, failure analysis.
- Ability to use AI to ramp fast on unfamiliar domains; agentic coding tools and frontier models are your working tools, not an add-on.
- Product sense, design taste, and extreme attention to detail.
- Extreme agency: you find the real problem, not just the stated one.
STRONG ADDITIONAL QUALIFICATIONS:
- Exposure to chip development, kernels, hardware, or manufacturing.
- Infrastructure, data engineering, or DevOps experience.
- Fine-tuning, RAG over proprietary technical data, or multi-agent orchestration.
- Startup experience, especially through hypergrowth.
