Product Operations Manager

Meta is seeking a Product Operations Manager to join our Product Operations Foundations team and drive model quality across all Meta surfaces. We are in the middle of a transformation, becoming an AI-driven, IC-led organization that scales through orchestration, deep product expertise, and technical excellence. Our team is building and operating autonomous agents that handle end-to-end workflows (triage, bug resolution, launches, dogfooding, evals) with minimal human intervention. If you're energized by owning complex quality programs end-to-end, building and operating AI-driven workflows, and driving measurable product improvements in a fast-paced environment, this role is for you.

You will be responsible for managing and evaluating our AI solutions and infrastructure to improve precision, prevent drift, and maintain real-time observability. This role is expected to set strategy for LLM models, determine areas of investment for increasing accuracy, advise leadership on impending risks, define roadmaps and reporting strategy to shape the future of our AI work. As part of this work, you will be expected to build and maintain industry-wide expertise, develop effective cross-functional relationships, advise engineering and cross-functional partners on areas of investment, determine staffing needs, and solution against critical bottlenecks.
Product Operations Manager, Model Quality Responsibilities
Defines the technical direction for model maintenance (retraining cadences, drift mitigation, performance recovery) and evolution (new capabilities, architecture improvements, multi-modal expansion). Translates cross-product performance patterns into investment recommendations for evaluation leads
Provides cross-product context, defines what good looks like at the model level, and informs evaluation methodology. Evals owners own execution of verification pipelines within their products; this role ensures consistency and identifies gaps across the portfolio while building institutional competence by surfacing performance patterns and proven methodologies, enabling evals captains' ability to execute and unblocking them as needed
Defines what leadership needs to see, how model health should be measured and reported, and what thresholds trigger escalation
Provides thought partnership to evals managers on narrative of model health, provides visibility into our classification strategy and accuracy measurement process
Works with evaluation managers to drive cross-app taxonomy alignment in alignment with cross-functional needs and advises on a strategy for the migration of LLM accuracy assessment to judges
Owns the consolidated view of all production model performance, identifies systemic patterns and emerging risks, and ensures leadership can verify model health on demand
Partners with AI Implementations, operational systems teams and the Metrics & Measurement team to build and maintain the infrastructure that surfaces this information
Establishes performance guardrails that evals captains implement. Continuously scans industry developments and best practices to incorporate into org-wide approach
Maintains a tight feedback loop with product and eng teams across apps to ensure alignment on production priorities and deployment risks
Deploys deep SME expertise to diagnose, unblock and directly resolve technical bottlenecks to complex model quality problems (atrophy, accuracy regressions, performance plateaus) when evaluation leads encounter blockers they cannot resolve independently
Drives alignment with cross-functional teams (quality and reliability partner teams) on tooling needs to support Product Operations classification strategy (ML classification tooling for initial-tier classification, user voice, breakdown graphs). Advocates for investment, flags risks, influences direction

Minimum Qualifications
Bachelor's degree in a directly related field, or equivalent practical experience
7+ years of experience in strategy, operations, consulting, or data analysis
Analytical experience using data to tell a story and influence product direction using intermediate to advanced SQL
Experience building or deploying AI/ML solutions, LLM model quality or automation in production workflows
Strong communication skills with ability to influence multiple cross-functional stakeholders and senior leadership
Experience breaking down ambiguous issues into component parts to develop solutions
Ability to design AI workflows that operate effectively within enterprise data sensitivity constraints, balancing quality and privacy principles

Preferred Qualifications
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
Experience operating in flat, IC-heavy org structures with high individual autonomy
Demonstrated history of evaluating industry best practices and providing organizational recommendations on approaches to AI models and development
Experience in product quality, QA, or technical program management
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)
Familiarity with LLMs, AI agents, or ML evaluation frameworks
Experience working with global/remote teams