Product AI Implementation Associate

About AUI

AUI builds Apollo, a neurosymbolic foundation model for AI agents that do consequential work, where accuracy, reliability, and visibility matter — conversations with customers, and the processes behind them. Instead of describing behavior in a prompt and hoping the model complies, Apollo compiles declared rules into a checkable program. Constraints are enforced by the system rather than requested of the model, so “this agent will never do X” is something you verify, not something you hope for. The language model works as a translation layer at either end — structuring what a person (or another agent, or a system) says into symbolic slots on the way in, and generating fluent responses on the way out.

That guarantee is what LLMs structurally can never achieve. We serve companies from startups to the Fortune 500, across regulated industries, retail, and travel — environments where an agent that improvises is a liability, and where someone has to answer for every action it takes.

Apollo-1 is a self-serve platform, and most teams can build on it themselves. Large enterprises — with deeper integrations and more involved internal processes — can use a hand getting their agents all the way to production. Our commercial team also needs proof-of-concept agents built for prospects, fast. That’s this team.

The role

You’re joining our deployment team, serving two constituencies.

Internal. Our product and commercial teams need working agents — proofs of concept for prospects, reference builds, agents that pressure-test the platform before customers find its edges. You’ll build them, often on short notice, often for a business you learned about today.

External. The most complex enterprise deployments, where the agent has to hold a real company’s rules across hundreds of scenarios and act on live systems. You’ll be in the room with their operators and subject-matter experts, not reading a requirements doc someone else wrote.

An agent starts life as a real business: a bank’s dispute policy, an airline’s rebooking rules, a retailer’s returns matrix. Someone has to sit inside that mess, understand how the business actually works, and turn it into a program the platform can enforce and certify. Then train it, grade it, try to break it, and sign off that it’s ready to act on a customer’s behalf.

The loop closes back into the product. What you solve by hand for one enterprise should become something the next customer can do themselves, and you’ll be one of the loudest sources of truth about what the platform can’t do automatically.

What you’ll do

• Sit with partner teams to extract how their business actually operates — including the rules nobody wrote down and the exceptions everyone knows about
• Author agent programs: translate policy, process, and edge cases into structured, unambiguous declarations the platform can enforce
• Run training rounds — design scenarios, simulate conversations, grade outputs, and iterate until behavior holds
• Own certification: prove a program does what it claims before it ships, and triage it when it regresses
• Find the contradictions. Real businesses run on rules that conflict. You’ll be the one who notices the refund window doesn’t square with the cancellation clause, and who drives the decision on which one wins
• Build proofs of concept alongside our commercial team, and support technical discovery with large prospects — you’re the person in the room who can answer “can it really do that?”
• Turn one-off solutions into reusable patterns, so the self-serve product absorbs what the deployment team learns
• Feed the field back to product: what’s slow, what’s missing, what breaks at scale

Who we’re looking for

We care more about how you think than where you’ve worked, with one hard requirement.

-Technically fluent — not a coder. You’ve worked inside a technological product environment and you understand how systems fit together: what an API is doing, where data moves, why an integration breaks, what engineers mean when they say something is hard. You use coding agents — Cursor, Claude Code, or equivalent — as a normal part of how you work, not as something you tried once. You won’t be shipping production code here, but you will be building, and the tools are the same.

-Opinionated about AI. You’re curious about it end to end: roughly how the models actually work, what they’re doing to industries and to work itself, where this is all going. We do…