Product Operations Manager, Meta Superintelligence Labs

Meta•Menlo Park, CA

About The Position

Meta is seeking a Product Operations Manager to join Meta Superintelligence Labs Product Operations and drive product quality, AI-powered automation, and operational excellence across MSL surfaces, specifically TBD Labs. 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.

Requirements

Bachelor's degree in a directly related field, or equivalent practical experience.
8+ years of experience in product operations, business operations, strategy, consulting, or technical program management within a technology or AI-focused organization.
Analytical experience using data to tell a story and influence product direction using intermediate to advanced SQL.
Experience building or deploying AI/ML solutions or automation in production workflows.
Experience communicating and influencing multiple cross-functional stakeholders and senior leadership.
Experience solving problems and breaking down ambiguous issues into component parts to develop solutions.
Familiarity with AI or ML product development lifecycles, including model evaluation, safety review, and deployment processes.
Track record of defining operational strategy in ambiguous, fast-moving environments and driving adoption across large cross-functional organizations.
Experience building or improving operational tooling such as project tracking systems, workflow automation, or data pipelines to support research or engineering teams.
Experience working within an AI research, machine learning, or large-scale model development organization.

Responsibilities

Investigating and communicating the quality of MSL products to engineering, product, design, and other cross-functional partners.
Owning AI builds for collecting, generating, and triaging reported quality issues.
Partnering with product and engineering teams to implement process and tooling improvements focused on driving developer efficiency, resolution of issues, and improved product quality.
Becoming an expert in the health and quality of MSL products, identifying trends, regressions, and user pain points through data analysis and hands-on product usage.
Owning end-to-end product quality programs for your area, including launches, dogfooding, RCAs, SEVs, and app health regressions automating first and operating with high reliability.
Designing, building, and operating autonomous AI agents and workflows for your product area: bug triage, prioritization, classification, deduplication, and resolution.
Partnering with engineering and product teams to drive resolution of high-priority issues and influence product roadmap based on quality insights.
Preparing communication materials and progress tracking for multiple audiences including delivering updates to leadership stakeholders.
Collecting, analyzing, and leveraging data to identify quality trends and inform product decisions; building dashboards and metrics to drive data-driven decision making.
Building and maintaining an evals practice for your area, ensuring AI solutions meet accuracy thresholds through LLM judges, labelers, and governance frameworks.
Assessing and improving existing AI solutions within your scope, driving migration to standard platforms and partnering with AI Implementations leadership on scaling decisions.
Coordinating with cross-functional teams and Foundations partners to align on org-wide priorities and share best practices.
Designing and operating AI workflows that comply with MSL data sensitivity guardrails, ensuring quality signals are collected and processed within encryption boundaries and privacy-first constraints.