AI Engineer, Product

Responsibilities

• Build and ship customer-facing features in production — from sketch to rollout to iteration based on real customer feedback.
• Define the data contracts between the agent and the rest of Brex's financial system of record, and evolve them as the product gets sharper.
• Stand up feedback and evaluation loops that let us quickly gather product signals and close them with product fixes — better signals, better surfaces, better workflows.
• Run experiments on customer-facing behavior (UI flows, prompt-driven product changes, new review modes) and make calls based on what the data says.
• Talk to customers and reviewers directly, bring what you learn back into the product, and prioritize what to build next on the team.

Requirements

• Strong track record of shipping customer-facing features end-to-end across both backend and frontend — you don't bounce work over a wall, and you've built enough of each to have real opinions.
• Product mindset: you reason about users, workflows, and outcomes first, and treat the system as the means to an end. Comfort with ambiguity and willingness to talk to customers directly.
• Strong bias towards action — you've operated in environments where the next thing to build wasn't handed to you in a ticket, and you've shipped things that didn't exist before.
• Comfort working across team boundaries and pushing back on decisions outside your direct ownership when the seam isn't right — agent design, UX, data model, all fair game.
• Strong backend foundation — system design, data modeling, API shape — and the disposition to ship the UI yourself when that's what the user problem needs, rather than handing it off.

Bonus

• Early-stage startup experience, or time on a small team where you owned a product surface end-to-end.
• Experience building products on top of LLMs or agentic systems — particularly the surrounding harness (evaluation, tracing, feedback loops, human-in-the-loop workflows).
• Background in fintech, compliance, audit, fraud, or other domains where review workflows and traceability matter.
• Experience operating where the underlying system is non-deterministic and the product has to compensate for that.
• Track record of being the engineer teams pull in when a project is stuck across backend, frontend, and a third system that nobody fully owns.