Summary
✨ AI‑Generated
A fast-moving AI infrastructure team is seeking a Product Engineer focused on Search to own a developer-facing search API end to end. You will translate retrieval and ranking research into production improvements, optimize latency and relevance, shape API behavior and documentation, and continuously use real developer feedback to remove friction. The role offers substantial autonomy and direct product ownership.
Highlights
High-autonomy product engineering role with end-to-end ownership of a developer-facing search API, direct impact on product quality and latency, and close connection to retrieval and ranking research.
Description
About this role
We are looking for a Product Engineer (Search) with 3–12 years of experience to own our client's developer-facing search endpoint — closing the gap between retrieval and ranking research and what developers actually feel in the API.
You'll be the single person who takes a ranking improvement from research and ships it as a live, polished developer experience in days, not sprints.
No PM, no hand-holding — just you, the product, and a fast-moving team building infrastructure for the AI era.
What will you be doing?
Own the search API end-to-end — response format, latency, error handling, filtering, and docs — as the sole accountable person for how it feels to developers.Translate retrieval and ranking research wins into shipped product changes developers notice (think: 200ms latency improvements, better recall/precision tradeoffs).Dogfood the API relentlessly — read every GitHub issue and Discord thread touching search and fix friction before users have to ask.Run fast product experiments: form a hypothesis, instrument it, ship it, measure it, and decide quickly with imperfect data.Define what good looks like independently — no PM to scope tickets; you set the priorities and own the outcomes.
Key Requirements
Production search experience (retrieval, ranking, relevance) at a developer-facing company — this is a hard requirement, not a nice-to-have.Has shipped on top of Elasticsearch, OpenSearch, Vespa, Algolia, vector databases, or a custom IR stack, and owned an externally consumed search API.Can talk fluently about BM25 vs.
semantic hybrid retrieval, re-ranking, and recall vs.
precision tradeoffs — unprompted.Founding engineer DNA: has shipped features end-to-end at a sub-200-person startup (Series A–C) without a PM, designer, or QA layer.TypeScript/Node.js proficiency; comfortable with observability tooling (Datadog, Sentry, OpenTelemetry) and cloud infra in production.
