Staff Product Manager, AI for Finance Admin (Remote)

Brex; New York (NY); On-site; USD 180,000 - 260,000; Full time

Benefits offered by this job

Medical, dental & vision

Generous vacation

Spring Health & Calm

Year-end company shutdown

Milk Stork for nursing parents

In-office days, lunches & offsites

Commuter benefits

4 fully remote weeks

Carrot Fertility benefits

Generous parental leave

Job summary

Brex is seeking an experienced product manager to lead the Finance Admin AI surface, guiding strategy, roadmap, and day-to-day shipping decisions for our core AI agents. You will partner with engineering, design, and research to shape agent behavior and evaluation frameworks that finance teams rely on to keep spend under control and numbers accurate.

You will translate complex finance workflows into practical agent capabilities, define success metrics, and push for rapid iteration from prototype

Qualifications

• Experience owning complex, user-facing products end to end.
• Ability to drive direction with limited specs and clear outcomes.
• Comfort prototyping, analyzing data, and deep-dive into implementation details.

Responsibilities

• Own product direction for the Finance Admin AI surface, roadmap, and day-to-day shipping decisions.
• Translate finance workflows (policies, approvals, close) into reliable agent behavior.
• Define what good judgment means for an agent handling company funds and build evaluation loops.

Skills

Product management

Ambiguity handling

Product judgment

Prompt design

Eval design

Fintech familiarity

Expense management

Accounting workflows

Technical depth

Job description

Brex is seeking an experienced product manager to lead the Finance Admin AI surface, guiding strategy, roadmap, and day-to-day shipping decisions for our core AI agents. You will partner with engineering, design, and research to shape agent behavior and evaluation frameworks that finance teams rely on to keep spend under control and numbers accurate.

You will translate complex finance workflows into practical agent capabilities, define success metrics, and push for rapid iteration from prototype