About the role
Requirements:
- 5-7 years of total engineering experience, with at least 2-3 years on production context pipelines, retrieval-augmented systems, or retrieval at scale.
- Has shipped a production retrieval or context system and run the evaluations that prove it works.
- Both halves are required; we will ask for the specific evaluation design and what it caught.
- Hands-on builder. You can sit down and ship the evaluation framework yourself this week, not delegate it.
- Strong systems thinker. Opinions on vectorisation choices, chunking strategy, retrieval ranking, hybrid search, and cost/latency trade-offs and the ability to defend them.
- Has built or owned at least two integration connectors end-to-end (OAuth, rate limits, normalisation).
- Comfortable in our stack: TypeScript, Node.js, PostgreSQL, vector storage (pgvector or equivalent).
- Strong adjacent stacks accepted if the ramp story is credible.
- Written communication strong enough to author RFCs that another engineer or an AI agent can build from without you in the room.
- AI-augmented engineering as a first-class part of your loop.
- If working through Claude (or equivalent) feels uncomfortable to you, this role is not a fit.
- Smart and curious first-principles reasoning, comfortable in ambiguity, learns the next thing fast.
- Bonus: experience with multimodal context (image or video embeddings, multimodal retrieval); has worked on agent / tool-calling systems and understands the evaluation question for tool calls; experience with multi-tenant context isolation; has worked with Model Context Protocol (MCP) servers; has mentored a junior engineer to ship-readiness.