Product Operations Manager, Model Quality

Product Operations Manager, Model Quality

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.

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

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 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