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Brex is seeking a Staff Product Manager for Growth to lead the re-architecture of its go-to-market (GTM) infrastructure with AI-powered systems. You will own critical GTM metrics and define product strategy to drive step-change improvements across the revenue funnel.
Key responsibilities include:
- Owning a critical GTM-related metric and defining the product strategy and roadmap to drive measurable improvements
- Identifying structural bottlenecks across the revenue funnel (tooling, automation, data, incentives, workflows) and designing systems to address them
- Leading 0→1 and 1→N initiatives that re-architect Brex's GTM infrastructure
- Partnering deeply with Sales, Marketing, RevOps, Engineering, Legal, IT, and Data teams to drive high-impact outcomes
- Influencing stakeholders and executives through clear strategy, strong business cases, and measurable results
- Establishing clear input and output metrics, operating with rigor on experimentation, prioritization, and tradeoffs
- Helping define and scale Brex's Growth Product function as it evolves
Initiatives include designing and deploying AI agents for prospecting, lead qualification, and customer onboarding; building intelligent outbound and lifecycle automation systems; improving ML-driven pipeline prioritization and scoring; optimizing application and onboarding flows; standing up experimentation infrastructure; and building revenue tooling platforms that compound productivity gains.
You will work in one of three offices (San Francisco, Seattle, or New York City) with a hybrid arrangement requiring three days per week on-site (Monday, Wednesday, Thursday). Remote work is permitted up to four weeks annually.
Required qualifications: experience building and scaling growth, revenue, or GTM systems in high-growth tech; track record of owning and materially moving company-level metrics; strong systems thinking; demonstrated leadership of complex cross-functional initiatives; deep analytical fluency with funnel metrics, experimentation frameworks, and data tools (SQL preferred).
