Product Manager, Enterprise AI

Meta Product Managers work with cross-functional teams of engineers, designers, data scientists and researchers to build products. We are looking for Product Managers who value moving quickly and lead AI-native product development at scale, with a focus on enterprise-grade agents.

Responsibilities

Is the primary driver for identifying significant near and long-term opportunities in a large Product area, and driving product mission, strategies, and roadmaps in the context of broader organizational strategies and goals. Generate buy-in and drive consensus across organizations. Bring clarity and structure to ambiguous opportunities. Consistently demonstrate initiative and execute with limited oversight. Critically evaluate when AI is (and isn't) the optimal solution at portfolio level, setting the standard for rigorous tradeoff analysis. Champion AI-native strategies including comprehensive evals and data strategies that enable org-wide continuous improvement. Drive product development with teams of engineers and designers, while maintaining team health. Work closely with cross-functional teams to drive product mission, define product requirements, coordinate resources from other groups (design, legal, etc.), develop roadmaps, and guide the team through key milestones. Reimagine workflows, responsibly using AI tools to transform team velocity and capability at organizational scale. Own Agent Core platform for Enterprise: agentic harness, orchestration, AI infra, and enterprise requirements prioritization by customer impact and blocking needs; define success, roadmap, and investment tradeoffs. Own enterprise product readiness from pilot to scaled rollout: migration and rollout plans tracked with scorecards, privacy and compliance readiness (data retention, deletion, PII redaction, tenant isolation), and closure on reliability, auth, safety, and SLAs with engineering and field teams. Establish evals as a first-class practice: eval harnesses, golden datasets and judges, task completion measurement driving continuous improvement. Lead AI infra and architecture tradeoffs with engineering: orchestration, retrieval and grounding, reasoning, latency, cost, auth. Lead cross-functional execution across Agent Core surfaces plus adjacent model, data, and go-to-market teams. Translate AI capabilities into enterprise visions that ship to real client outcomes. Communicate strategy and report enterprise product performance with radical clarity to clients, field teams, and leadership.

Qualifications

Demonstrated proficiency using AI-enabled tools to build product artifacts at scale 10+ years of experience working collaboratively with engineering, design and user research teams 10+ years product management and/or Product Design Experience developing and championing AI-native strategies across organizations BA/BS in Computer Science or related field Seasoned Product Management experience shipping enterprise or platform products at scale Experience taking a 0-1 platform from strategy through enterprise adoption and full product lifecycle Experience turning diverse enterprise requirements and customer requests into a single coherent product strategy Experience with agentic harness and AI infra plus LLM evals: orchestration, tool use, golden datasets and judges, task completion measurement Technical depth with data and infra: analyze complex datasets, lead credible architecture tradeoff discussions on reasoning, latency, reliability, cost Communication and influence: radical clarity from exec to eng, AI-native builder who prototypes and ships with AI tools Shipped enterprise-grade AI infra or agentic products: harness, reliability, latency, security and auth, SLAs, readiness Redesigned workflows with AI tools to measurable impact on quality or task completion Worked with field, solutions, or partnerships teams on enterprise adoption 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