Etched
Internal Acceleration, Research Engineer (Kernels)
San Jose · Senior
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About the role
- You'll work on harnesses, tool use, context, and memory, execution environments, and evals that let agents conduct long-running engineering work that is useful.
- 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.
Responsibilities
- Embed with our kernels team 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.
- Build execution environments where agents can verify their own work, with sandboxed access to simulators, EDA flows, compilers, profilers, and real hardware.
- Measure what works. Design evals and track adoption so we double down on the tools that matter and kill the ones that don't.
- Ship full-stack internal products from zero to one: the interfaces, data pipelines, and infrastructure that define how Etched builds.
- 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.
- Relocation support for those moving to San Jose (Santana Row)
Nice to have
- Have at least 1-5+ years of experience building and shipping software end to end, ideally at a fast-moving startup
Benefits
- Medical, dental, and vision packages with generous premium coverage
- $500 per month credit for waiving medical benefits
- Various wellness benefits covering fitness, mental health, and more
- Plus Significant Equity
Company info
- We are the first inference-focused frontier AI system, betting early on transformer and transformer-like architectures and on increasing model sizes.
- We are a fully in-person team in San Jose (Santana Row), and greatly value engineering skills.
This listing is sourced directly from Etched's careers page and normalized into a canonical job model.