Program Manager, Product Data Operations (Reality Labs)

Meta•Seattle, WA

3d•$99,000 - $144,000•Hybrid

About The Position

Meta is hiring a Reality Labs Product Data Operations Program Manager to run multilingual data collection for language expansion work. Reality Labs builds mixed reality, virtual reality, and wearable products. As Meta expands AI experiences into more languages and regions, multilingual data quality, coverage, and speed help determine how well models understand and respond to people worldwide. The role takes model and product requirements from intake through delivery, selecting in-house, scaled vendor, or hybrid collection models for each request. It also establishes scalable, repeatable data collection practices across languages and locales. The Program Manager works with research, engineering, product, quality, legal/privacy, and vendor partners, and defines and enforces data quality standards in a fast-moving environment with shifting priorities.

Requirements

Currently has, or is in the process of obtaining, a bachelor's degree in a directly related field or equivalent practical experience; degree must be completed before joining Meta.
4+ years of experience in program management, data operations, or technical operations in a data-driven, AI/ML, or technology organization.
Experience running data collection, annotation, or language data programs, including managing external vendors at scale.
Experience communicating with and influencing multiple cross-functional stakeholders and leaders.
Experience defining and maintaining quality standards and acceptance criteria for data deliverables.
Analytical experience using data to identify trends, diagnose operational issues, and influence program direction.
Experience prioritizing competing workstreams based on impact and deadlines in fast-moving, agile environments with changing priorities.
Experience breaking ambiguous problems into component parts to develop solutions.
In-person presence at least 3 days per week, with occasional travel required.

Nice To Haves

Experience owning program budgets, cost forecasting, or per-unit cost tracking.
Experience with AI/ML development lifecycles, including model evaluation, benchmarking, and data-centric improvement of model output.
Proficiency in one or more languages besides English.
Track record of building scalable, repeatable operating models or playbooks for a service function.
Experience designing hybrid in-house and vendor sourcing strategies, including cost/quality trade-off analysis.
Experience with participant recruitment, screening criteria, informed consent, and data governance for human-subject data across regions.
Experience with multilingual, speech, text, or conversational AI data, including localization or language expansion programs.
Experience building or deploying automation or AI solutions in production workflows.
Experience in AR/VR, wearables, consumer electronics, or AI products.

Responsibilities

Own the full lifecycle of language expansion data collection programs, from requirements intake and scoping through collection, quality assurance, and dataset delivery.
Translate model, evaluation, and product requirements into collection specifications, including language and locale coverage, speaker and participant criteria, volume targets, timelines, and acceptance criteria.
Select an in-house, scaled vendor, or hybrid data collection model based on quality, speed, cost, coverage, and risk, and explain tradeoffs to stakeholders.
Build repeatable data collection playbooks, SOPs, templates, and intake standards to support consistent launches across languages and locales.
Work with research scientists, engineers, TPMs, product, linguists, quality, and privacy partners to align priorities, dependencies, and success measures.
Develop systems to track throughput, cost, and quality trends, and build dashboards and leadership-facing reporting to support decisions.
Prioritize competing requests based on impact, urgency, and stakeholder needs; adapt plans as model training schedules change, escalate risks early, and meet budget targets.