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Applied AI Engineer

Open 15d

We are hiring an Applied AI Engineer to own the conversation layer of an AI-first fundraising platform: the agents that handle every reply, carry conversations through to booked meetings, and look after every relationship across our warm outreach, our investor network, and investor relations.

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, live with paying clients, profitable and self-funded, built by a small, senior, flat team that ships fast.

The role

You own how Paires converses: every reply handled well and in time, booking, nurture, and the coaching experiences we are building on the same data. It starts as our reply and messaging system and grows into a core part of the product. You will also build broader applied-AI features across the platform. What this seat is not: a research seat. An internal knowledge bot or a RAG demo is not the bar. We want people whose systems hold real conversations with real people outside their team, day after day. Cold outreach from nothing is a different seat too - here the conversations already exist, and your agents carry them.

What you will own

  • Agents that read inbound messages and draft the right reply, fast, with a human in the loop.

  • The messaging layer across our warm outreach, our warm investor network, and investor relations, not just one inbox.

  • Conversational agents end to end: replies, booking, nurture, and support that reads human.

  • The eval spine that gates quality: golden sets, judges, and the guardrails that hold as the conversations grow.

  • Broader applied-AI features: classification, extraction, routing, summarization.

You are a fit if you

  • Ship conversational LLM systems to production with real users: reply handling, support, booking, or nurture agents. Inbound work counts fully here.

  • Have built evals yourself: golden sets from scratch, judge criteria, ship or rollback decisions made on the numbers.

  • Have integrated LLMs with email, CRM, or messaging systems.

  • Think like an operator, and know the business goal behind the message.

  • Are an engineer first. We run roughly 80/20 engineering to research.

  • Move fast with AI tooling and own outcomes.

  • Our stack: Python, Supabase, Pydantic AI, Claude Agent SDK, AWS. If you have shipped on any of it, lead with that. Bonus: email and CRM integrations, RevOps exposure, sales or IR experience.

What we offer

Fully remote and async. Your day overlaps with US Eastern time for a few hours - not full US hours. Meetings batch on Mondays and Thursdays, the rest is deep work. The best AI tooling, paid (Claude Code, Cursor, top models).

How to apply: hit apply, which takes you to our short application form. We read every application.

What this application asks

ashby

Name, Email, Resume

  • Drop a link or description to the thing you have built that you are most proud of (live product, repo, launch). If your best work is closed or confidential, say so and also link anything live you personally own. What exactly was YOUR contribution, and what is one decision you made in it that you would still defend today? Tell us who used it and what it did for them. written answer
  • How do AI tools actually fit into your daily workflow right now? What have you automated, what do you refuse to automate, and what is one thing you changed in your AI stack in the last six months - and why? written answer
  • Describe an AI system you built that wrote or handled messages for real recipients (outreach, support, chat, email). Who received them (customers, prospects, colleagues), roughly how many a day, and what was the reply or resolution rate? What guardrails stopped embarrassing outputs, and what is one failure it still produced? If no system of yours has ever sent words to a human recipient, say so straight - do not describe a different system. An honest no is not a dealbreaker. written answer
  • Tell us about the AI system you have built that had the clearest business impact (revenue, conversion, response time, retention, cost). What was the metric before and after, what did you build, and how did you prove the impact came from your system and not something else? written answer

See also

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