
Reality Labs Product Data Operations Program Manager
Required skills
- Reality Labs
- Product Data Operations
- Program Manager
- data collection programs
- AI-powered automation
- model and data evaluation
- capture facility
- mixed reality
- virtual reality
- wearables
Nice to have
- AI development lifecycles
- ML development lifecycles
- model evaluation
- benchmarking
- deployment processes
- scaling a lab
Role summary
Meta is seeking a Reality Labs Product Data Operations Program Manager to oversee data collection programs, AI automation, and model evaluation for next-generation hardware products. This role involves translating research and engineering needs into collection protocols, deploying AI/automation tools, and ensuring data quality and reproducibility. Responsibilities include managing end-to-end data collection, establishing quality processes, building evaluation practices, analyzing operational data for decision-making, resolving risks, and managing vendors. The position requires hands-on operational work with prototype hardware and AI workflows, with a hybrid work model requiring 3 days onsite per week.
Responsibilities
- Own end-to-end execution of on-site data collection programs at the capture facility and across additional labs and remote participant sessions, from requirements intake through dataset delivery and acceptance
- Partner with research scientists, engineers, and technical program managers to translate model and evaluation requirements into collection protocols, participant criteria, and acceptance criteria
- Lead, schedule, and develop teams of on-site collection moderators and technicians, and author the standard operating procedures, training curricula, and certification materials they work from
- Set up, operate, and troubleshoot prototype and pre-release capture hardware, sensor rigs, and multi-device synchronization setup
- Establish and operate quality processes, including calibration, in-session validation, and post-collection audits, and ensure that collections meet participant consent, privacy, and safety requirements
- Design, build, and operate AI and automation solutions across the data pipeline, including pre-annotation, quality triage, and workflow automation that reduces manual effort and increases throughput
- Build and maintain evaluation practices for your area, including gold-standard datasets, model versus human comparison, and the accuracy thresholds that determine whether data and AI outputs meet the bar
- Collect, analyze, and leverage operational data to identify quality and throughput trends, building the dashboards, metrics, and leadership-facing reports that drive decision-making across your programs
- Drive the resolution of risks and blockers across hardware, tooling, vendor, and scheduling dependencies, re-prioritizing as hardware and model training schedules evolve
- Manage external vendors supporting participant recruitment and onsite staffing, and align requirements, timelines, and budgets across stakeholders and facility capacity
Minimum Qualifications
- 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 prior to joining Meta
- Bachelor's degree in a directly related field, or equivalent practical experience
- 4+ years of experience in program management, operations, or technical operations within a hardware, research, or data-driven organization
- Analytical experience using data to identify trends, diagnose operational issues, and influence program direction
- Experience building or deploying automation or AI solutions in production workflows
- Experience in communicating and influencing multiple cross-functional stakeholders and senior leadership
- Experience solving problems and breaking down ambiguous issues into component parts to develop solutions
- and requires in-person presence for a minimum of 3 days per week, with occasional travel to other lab sites
Preferred Qualifications
- Familiarity with AI or ML development lifecycles, including model evaluation, benchmarking, and deployment processes
- Track record of standing up or scaling a lab, studio, or collection facility, or growing a service function's book of work in an ambiguous, fast-moving environment
- Experience collecting data for machine learning applications, including ground-truth capture, dataset curation, or model evaluation
- Experience with multi-sensor or multi-device synchronized capture, sensor calibration, or biometric and physiological sensing
- Equivalent experience from an adjacent physical collection domain such as clinical research operations, motion capture or volumetric studios, autonomous vehicle or robotics validation, human factors labs, or new product introduction
- Familiarity with participant recruitment, screening criteria design, informed consent, and data governance for human-subject data
- Experience in AR/VR, mixed reality, wearables, or consumer electronics, or in another hardware-based development environment
- Experience in owning program budgets, cost forecasting, or per-unit cost tracking
$99,000/year to $144,000/year + bonus + equity + benefits
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