AI Context & Data Infrastructure Engineer

Town is building the AI coworker for every team. Everyone gets their own Townie, an AI assistant that learns how you work (your voice, judgment, relationships, and priorities) and does real work on your behalf across email, calendar, docs, Slack, and CRM. Townies don't wait to be prompted. They observe, learn, and act. And because every teammate has one, Townies can ask each other questions and hand off work, so the whole team moves faster together.

Four months after our public launch, we became a $1B company. We recently raised a $108M Series B, co-led by Forerunner Ventures and Menlo Ventures, with continued support from Andreessen Horowitz, First Round Capital and angels such as Zach Perret, Soleio Cuervo, Scott Belsky, and more. Our founders, Jean-Denis and Tony, previously led EPD at Plaid and applied AI product at Google.

Town is building the most personalized, most capable AI assistant for everyone — and personalization at that level is a retrieval and data problem. The assistant is only as good as the context it can bring into the moment: the right memory, message, document, or relationship, pulled fast and related by meaning across everything a person and their team touch.

You'll build the foundation the whole product reasons over: the search and data infrastructure behind that context. One shared retrieval layer combining lexical and semantic search, the realtime and batch pipelines that keep it fresh and correct, and the durable data model everything else is built on.

This is greenfield and high-leverage: you'll be the first person building this layer.

• Build the search and retrieval layer that puts the right context at every Townie's fingertips, the moment it's needed — one shared layer the whole product pulls from instead of refetching context on its own.

• Combine lexical and semantic search and own the tradeoffs between them: vector vs. lexical, precompute vs. fetch, hybrid retrieval, and ranking.

• Design the durable data model the assistant's work is built on, so context is relevant, fast, and cost-effective.

• Build and operate the pipelines behind it — realtime/streaming and batch — that keep the index fresh and correct as the underlying data changes.

• Stand up the indexing and storage layer and keep it fast and reliable at scale: latency, cost, freshness, and completeness.

• Lay the groundwork for relating content by meaning across everything the assistant knows — the start of a knowledge graph of people, companies, projects, and how they connect.

• Have significant, hands-on experience across lexical and semantic search components and approaches (BM25, embeddings, ANN/vector indexes, hybrid retrieval, ranking).

• Have run large-scale data infrastructure, ideally both realtime/streaming and batch — pipelines, indexing, and storage.

• Can make retrieval fast and cheap at scale, and reason about the latency, cost, and freshness tradeoffs cold.

• Are a systems thinker comfortable in greenfield, where the foundation doesn't exist yet.

• Are excited to take these systems from rapid prototype to production scale.

• Bonus if you've worked on ranking/relevance, knowledge graphs, or retrieval for LLM or agentic systems.

San Francisco, CA. Five days a week in person at our Financial District office.