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# Staff Backend Engineer

- **Company:** TOWN.COM, INC.
- **Location:** New York, NY, United States
- **Salary:** $250,000.0 - $300,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Distributed Systems, Graph Database, Large Language Models, Multi-Agent Systems, Backend, Virtual Agents, Data Pipelines
- **Published:** July 12, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=ce49b8831a911cdc

## About the Role

* Are comfortable defining technical direction in ambiguous, fast-moving environments - not just executing within one
 * Think in systems - distributed systems, data pipelines, APIs, reliability. You're drawn to infrastructure-level problems and the tradeoffs that come with them.
 * Have tuned your AI-native coding workflow that makes you dramatically faster and more effective.
 * Have experience with LLM infrastructure, agentic systems, or real-time AI workloads in production
 * Care about trajectory over tenure. We're less interested in years of experience than how fast you got here and what you built along the way.
 * Are excited about working in-person in SF, five days a week. We believe it matters at this stage.

## Description

The core technical challenge at Town is: context from everywhere, action anywhere. The assistant pulls from your email, calendar, Slack, docs, and connected tools to build a deep understanding of who you are and what you need - then executes reliably across all of them.

Town's AI assistant goes beyond a solid harness and decent tool integrations. It has memory that compounds over time, a suggestion engine that discovers what to automate before you think to ask, and network effects where your assistant gets better because your colleagues are using Town too. These are the systems you'll build.

You'll be a foundational hire, working across the full backend - from LLM orchestration and workflow execution to the identity and knowledge layer that makes every interaction smarter than the last. Everyone has direct influence over architecture decisions. There's no platform team to hand things off to - you own it end to end. The environment is greenfield, and the problems are unlike traditional backend engineering: non-deterministic, cost-sensitive LLM workloads; real-time personalization across a growing knowledge graph; agent-to-agent protocols that work across organizational boundaries.


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