AI Context & Data Infrastructure Engineer

Town.com, Inc.; San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Town.com, Inc. is building the most personalized AI assistant for everyone, starting from who you are. This role focuses on the search and data infrastructure that powers context, memory, and relationships across tools and documents.

You will own a shared retrieval layer that unifies lexical and semantic approaches to keep data fresh and relevant. You'll design the durable data model for context, build the pipelines behind it, and ensure the indexing and storage layer remains fast and reliable

Qualifications

• Significant hands-on experience with lexical and semantic search.
• Experience building large-scale data infrastructure for real-time and batch processing.
• Ability to optimize retrieval latency and cost at scale.
• Systems thinker comfortable with greenfield projects.

Responsibilities

• Build the search and retrieval layer used by the product.
• Combine lexical and semantic search and manage tradeoffs.
• Design durable data models to support context.
• Build and operate real-time and batch pipelines.
• Stand up indexing and storage layer for speed and reliability.
• Lay groundwork for knowledge graph connections.

Skills

Lexical search

Semantic search

Embeddings

Data pipelines

System design

Tools

BM25

Embeddings

ANN indexes

Hybrid retrieval

Ranking

Job description

About Town

Town (town.com) is AI that starts from who you are. We build a persistent model of your identity, your voice, your judgment, your relationships, and your priorities, and use it to do real work on your behalf across every tool where you operate: email, calendar, documents, Slack, and more. Town doesn't wait for you to prompt it. It observes, learns, and acts. The more you use it, the more it becomes an extension of you.

Town was founded by Jean-Denis Greze (CEO), former CTO of Plaid, and Tony Vincent (CPO), former Director of Applied AI Product at Google. We're a small, talent-dense team backed by Andreessen Horowitz, Forerunner Ventures, First Round Capital, and Conviction, with more than $73M raised to date.

About the role

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.

What you'll do

• 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.

You might thrive here if you...

• 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.

Location

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