Internal Acceleration, ASIC
Etched is hiring an Internal Acceleration, ASIC in San Jose, United States. It pays $150k-$400k a year and Level rates it ; you can apply on Level.
AI in this role
Job Summary
Our Internal Acceleration team’s mandate is to build agents to accelerate the production of frontier tokens.
You’ll work directly with our founders and embed with the teams building the product (kernels, ASIC, hardware, production, and special projects) to build new agentic harnesses and operating systems to transform the way hardware is built in an AI native world. Your work will vary widely, and your success is measured by how much faster Etched moves, not by the lines of code you write.
This role is for engineers who thrive on ambiguity, have real product sense, and want to own problems end to end. It’s a whiteboard for future founders. The problems we solve are time-sensitive and business-critical. This role will not be a 9-5, and the ideal candidate is excited about that.
You may be a good fit if you
Have at least 1-5+ years of experience building and shipping software end to end, ideally at a fast-moving startup
Have strong full-stack skills. We care less about whether you write every line yourself and more about whether what you ship works
Have built LLM agents or AI tooling that real users depend on, which means more than calling an API: context engineering, tool integration, evals, and failure analysis
Use AI to ramp fast on unfamiliar domains. Agentic coding tools and frontier models are how you work, not an add-on
Have product sense, design taste, and extreme attention to detail
Have extreme agency: you find the real problem, not just the stated one
Key Responsibilities
Embed with kernels, ASIC, hardware, and production teams to find the highest-leverage bottlenecks, and own each one from first idea to a tool engineers rely on every day.
Build and deploy LLM agents wired into our engineering workflows via tool-calling and MCP.
Ship full-stack internal products from zero to one: the interfaces, data pipelines, and infrastructure that define how Etched builds.
Measure what works. Design evals and track adoption so we double down on the tools that matter and kill the ones that don’t.
Take on special projects directly with the founders, surfacing problems across the company and doing whatever it takes to hit critical milestones.
Strong candidates may also have
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
Benefits
Medical, dental, and vision packages with generous premium coverage
$500 per month credit for waiving medical benefits
Housing subsidy of $2,500 per month for those living within walking distance of the office
Relocation support for those moving to San Jose (Santana Row)
Various wellness benefits covering fitness, mental health, and more
Daily lunch and dinner in our office
Unlimited compute budget subject to ROI justification
Base Compensation Range
$150K – $400K+
Plus Significant Equity
How we’re different
Etched believes in the Bitter Lesson. We are the first inference-focused frontier AI system, betting early on transformer and transformer-like architectures and on increasing model sizes. Our addressable market is the entirety of inference, unlike many of our competitors.
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 and work across disciplines as needed.
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.
How we rate this
Internal Acceleration, ASIC at Etched rates 89 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: RAG, AI agents, and Fine-tuning. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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