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Brex

AI Engineer, Product

San Francisco, California, United States$171k–$240kmidAdded 1 month ago

About this role

Join Brex's AI Engineering team to build the product experience around an Audit Agent that automates financial spend reviews. You'll design workflows, data flows, and interfaces that make AI-generated insights trustworthy and actionable for customers, working fluidly across backend and frontend.

What you'll do

  • Build and ship end-to-end customer-facing features from design through rollout and iteration
  • Define data contracts between the AI agent and Brex's financial systems, evolving them as the product scales
  • Create feedback and evaluation loops to gather product signals and drive improvements
  • Run experiments on UI flows and product changes, using data to inform decisions
  • Engage directly with customers and reviewers to understand needs and prioritize development

What they're looking for

  • Full-stack engineering (backend and frontend)
  • System design and data modeling
  • API design
  • Product thinking and user-centric design
  • Experimentation and data analysis
  • Cross-functional collaboration
  • Customer research and feedback integration
  • AI/agent system understanding

Benefits

  • Hybrid work: 3 days in San Francisco office per week
  • Up to 4 weeks fully remote work annually
  • Work on cutting-edge AI agent products
  • Collaborate with leading finance and AI teams
  • Direct customer impact at scale
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Brex

Brex builds mission-critical financial infrastructure and credit decisioning systems that power banking and spending management for companies globally. The company is hiring Backend Software Engineers II to develop scalable systems including underwriting engines, real-time data pipelines, and ML-powered credit models across 200+ markets.

Website
brex.com
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Likely interview questions

  • Tell us about a time you shipped a full-stack feature from backend to frontend end-to-end. What was the feature, and how did you make decisions about which layer to focus on?
  • How would you approach designing the data contracts and APIs between an AI agent's outputs and a customer-facing UI where users need to trust and act on the agent's reasoning?
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