Job Description

Want to build the infrastructure that makes AI agents actually work inside real companies?

AI models are powerful, but they're generic. Enterprise workflows aren't. This team is solving that gap, building a learning layer that turns messy internal context into structured, executable systems that AI agents can actually use.

You'll join a deeply technical team working on a platform that learns from tickets, Slack, emails, logs, and knowledge bases, then converts that into versionable "skills" for AI. Think of it as a "GitHub for context", a system that makes company knowledge readable, maintainable, and executable.

This isn't model training. It's everything that makes models useful in production.

You'll design and build the backend systems that power this layer, APIs, data models, integrations, and tooling that connect into real enterprise environments like ServiceNow, Jira, Zendesk, and Salesforce. The platform is already operating at serious scale, processing vast amounts of operational data across large organisations.

The work is high ownership. You won't be handed tickets. You'll take problems from idea to production, shaping architecture, building systems, and seeing how they perform in real-world use.

Your focus will include:

• Building backend systems in Python (FastAPI), from API design through to database schema
• Creating integrations, connectors, and data pipelines across enterprise tools
• Developing internal tooling and libraries that improve engineering velocity
• Owning systems end-to-end, including deployment and observability

You'll enjoy this if you care about how software is built. Strong typing, clean interfaces, and well-structured data models aren't afterthoughts here, they're core to how the team works.

You're likely someone who takes pride in designing schemas properly, enjoys building systems other engineers rely on, and prefers thoughtful, robust solutions over quick fixes.

The company has raised a $28M Series A led by Sequoia and is already working with large enterprise environments, processing data at significant scale. It's still early enough that your decisions will shape the platform and engineering culture long-term.

Comp: Base salary of $190K - 250K + meaningful equity
Location: New York (also expanding in London)

If you're interested in building the systems that make AI actually usable in the real world, this is worth exploring.