We're building AI agents that make every cross-functional discipline at Meta — Finance, Legal, People, Policy, Comms, Partnerships, Sales — 10x more effective. The ATA XFN team operates as a portfolio of startups inside Meta. Each pod embeds directly into an XFN organization, discovers high-value workflows, builds AI agents that collapse them, and creates feedback loops that continuously improve our models. We focus on effectiveness, not just efficiency — building tools that change what XFN professionals can do, not just how fast they do it. The XFN Pod Group PM owns product strategy for a large product area within one one of 7 XFN disciplines. You work directly with XFN domain experts (lawyers, finance analysts, recruiters, policy specialists) to discover their highest-value workflows, define what agents should do, set quality rubrics, drive adoption, and measure impact. You prototype alongside engineers. You ship weekly demos. You own the customer relationship for your product area end-to-end.

Qualifications:

8+ years of product management experience, including platform or developer-facing products Experience defining APIs, SDKs, or developer tools Track record of growing a platform ecosystem and managing partner relationships Strong technical judgment and ability to engage deeply with engineering teams Experience prioritizing across competing partner needs at scale Analytical rigor and ability to define success metrics for platform health Experience with AI/ML platforms or agentic systems Background in developer tools, infrastructure products, or internal platforms Experience building self-serve onboarding and documentation at scale Familiarity with plugin/extension architectures Domain expertise in one or more XFN disciplines (Finance, Legal, HR, Policy, Comms, Partnerships, Sales) Experience working in 0-to-1 environments with high ambiguity Experience with regulated or compliance-sensitive domains (SOX, legal privilege, data privacy) Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies