Senior Backend Engineer - CRM Team

The CRM team owns the surface Close customers live in all day: leads, contacts, opportunities, tasks, activities, custom objects, and the search that makes any of it findable. Everything upstream — calls, emails, meetings — flows into the data this team structures, and everything downstream, including every agent we ship, depends on it.

The team is making significant strides on the next iteration of Close's core data model, the largest engineering effort underway at the company.

We're reopening assumptions made a decade ago and asking what a modern CRM's data model should actually be — one where any small, scaling business can map their operations into Close their way. This is a long series of migrations and rearchitecture projects. The first of them are already underway. If you like greenfield and you like hairy, this is both at once: help design the next version of Close, and untangle the current one to get there.

One thing to know up front: we do move people between teams as the work shifts. Most engineers here end up on more than one team over their time at Close — this team is where you'd start, but over time you'll likely have the opportunity to work on many different projects.

You are

• A seasoned engineer. Python is our backbone, but perhaps you've worked across Go, Rust, or TypeScript. You pick the right tool for the workload rather than retreating to what you know.

• Architecturally minded. This is the thing we care about most. You've designed systems others built on, planned a migration that couldn't take the product down, and know how to sequence a rearchitecture into pieces that each ship on their own.

• Drawn to data model and retrieval problems. Schemas, relationships, normalization, query design, type systems. The plumbing that decides whether a CRM feels fast or feels like a database someone left running.

• Experienced in search — a serious bonus. Elasticsearch especially, and anything adjacent: Lucene internals, query planning, relevance, indexing at scale, building or operating a search engine rather than just calling one.

• AI-native in production. You've shipped meaningful LLM-backed or agentic features to real users, and you have a POV on where AI earns its place in a CRM workflow and where a structured system still wins.

• Working with AI in your day-to-day. You use AI tools in your own workflow to ship faster, write tighter code, and reason about unfamiliar parts of the codebase. We fund best-in-class developer tools and treat experimentation as part of the work.

• Battle-tested. You've debugged incidents where latency budgets didn't hold, owned a system everyone else relied on, or carried a pager for something with real customer impact.

• A builder first. You'd rather get a sloppy v1 in front of fifty customers than spend three weeks on abstractions. You ship.

You will

• Rearchitect the core CRM data model. Rework how leads, contacts, opportunities, and custom objects relate to each other so customers can represent their business the way it actually works.

• Make Close genuinely usable by agents. The Agents team builds the MCP foundation; we build the tools and endpoints on top of it that expose the CRM's data and actions. The hard part is search again: any MCP-compatible harness should be able to traverse, filter, and reason over a customer's CRM without learning our private query language first.

• Ship AI-native CRM features. This team built agentic enrichment: instead of paying a third-party data provider, an agent goes and researches whatever field you need, in bulk or automatically inside a workflow. There's a lot more of this ahead, including AI-powered data cleaning and richer AI search.

• Keep the hot paths fast. Finding a lead, updating a deal, scanning a pipeline, running a bulk action. Salespeople do these hundreds of times a week, and no amount of clever architecture matters if they're slow.

Tech you'll touch: Python, Flask, FastAPI, MongoDB, Elasticsearch, PostgreSQL, Redis, TaskTiger, MCP — plus whichever LLM ships next.