AI Tooling Engineer

We're hiring Engineers dedicated to building AI-powered tools that change how work gets done inside the company. You'll embed with teams across operations, customer success, and finance, find the work that's slow and manual, and ship AI agents and automations that actually get adopted.

This is a build-fast, talk-to-humans, own-the-problem role - not a research role and not a pure-infrastructure role. You'll define what "internal AI" looks like here from scratch.

You're energized by ambiguity and ownership. You'd rather ship a rough tool this week, watch someone use it, and improve it than spend a month on a perfect spec. You like sitting with a non-technical teammate, understanding their actual workflow, and turning it into something that saves them hours. You're comfortable being the only person who owns a problem end to end.

• You'll partner closely with the CEO and founders on identifying the highest-leverage operational problems to solve and shaping where internal AI goes next.  This is a rare amount of executive access to an engineering role. 
• You’ll work alongside functional leaders across the business to understand their priorities, surface opportunities , and align on what to build.  Your work will be visible and leadership level from day one. 
• Operations, Customer Success, Finance, People/Talent Acquisition, Engineering, IT and Data teams, these are your internal customers and collaborators.  You'll embed with them, and learn their workflows and ship tools they actually use. 

Examples of the kind of work waiting for you:

• An agent that drafts customer success replies from our knowledge base and ticket history
• A Slack-based workflow that routes and pre-fills finance approvals
• An internal eval harness so we can trust the LLM features we ship
• A tool that lets the ops team query [internal system] in plain language

You won't do all of these at once — you'll pick the highest-impact one, ship it, and move to the next.

• 1–3 years building software — backend services, internal tools, automations, or API-driven apps. Internships and co-ops count.
• Strong in TypeScript and/or Python.
• You've built something with LLMs — an agent, a RAG pipeline, a copilot, a prompt-driven tool — through work, a side project, or coursework. We care that you've shipped, not where.
• Comfortable wiring systems together: APIs, webhooks, SaaS integrations, async workflows.
• You can talk to non-technical stakeholders, figure out what they really need, and drive adoption — this matters as much as the code.
• You move fast under ambiguity and iterate from real feedback.

• Built copilots, agents, or workflow automations before
• Used an orchestration framework (LangChain, Temporal, or similar)
• Worked with vector databases or retrieval systems
• Integrated with Salesforce, Slack, Zendesk, Notion, or Jira
• Startup / high-growth experience

• First few weeks: shipped your first internal tool and it's in active daily use by a business team.
• Within 2 months: 2–3 tools live, and stakeholders are coming to you with ideas.
• By month 3: you've set the team's baseline practices for evals, prompt management, and safe LLM deployment — the foundation the function grows on.