Founding AI Engineer
PairesWe are hiring our Founding AI Engineer to own the agent layer of an AI-first fundraising platform: the conversational agents that run our warm outreach and investor relationships, and the matching engine behind them.
Paires is where founders come to raise capital. We pair them with the right investors from a large, engaged global investor network, then our agents run the warm outreach and manage the relationships that turn into meetings. It is a two-sided platform, a product for founders and a living network on the investor side, not one-way matching. We are live with paying clients, profitable and self-funded, and we run as a small, senior, flat team that ships fast.
The role
You own our core: the agent system that runs the product - agents that research investors, draft outreach that reads human, and carry investor conversations end to end - plus the matching engine that pairs founders with investors behind them. You build it, you scale it, and you run it. This is the most important surface in the product, and it is yours.
What you will own
The agent layer: one orchestrator routing work to specialist agents. Those agents research investors, draft outreach that reads human, and carry investor conversations end to end. Evals gate what they are allowed to do alone. This is where you start.
The matching engine behind it: embeddings, ranking, scoring, and the feedback loop that makes matches sharper over time.
The backend and data behind it all: Python, Postgres, Supabase, AWS, clean pipelines. The agent layer is built on the Claude Agent SDK and Pydantic AI.
Shipping to production end to end, and the judgment to know what to build next as we scale.
You are a fit if you
Have been one of the first engineers at a quick-growing company, running the show: you know what it takes to scale and deliver a product, not just manage it.
Have at least 3 years of shipping production software behind you, and have taken a full app from zero to production and kept it running, end to end. This seat needs a whole-product builder, not only a model specialist.
Have shipped LLM systems to production and have real examples you can walk us through, including an agent system that is more than a single prompt in a loop.
Have built retrieval, ranking, or matching with embeddings in production.
Write strong Python and reason clearly about data and systems.
Design
Opens the company's application page
Listed via
Himalayas
himalayas.app
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