Reality Labs Product Data Operations Program Manager

Meta is seeking a Reality Labs Product Data Operations Program Manager to drive data collection programs, AI-powered automation, and model and data evaluation at our capture facility. Reality Labs builds the next generation of computing across Meta’s Hardware Products mixed reality, virtual reality, and wearables, and capabilities like hand tracking, eye tracking, body tracking, and holograms dependent on machine learning models trained on high-quality human data.
Our scope spans the full lifecycle of the data: running the onsite collections that produce it, deploying AI and automation to scale how it is processed and checked, and building the evaluation practices that measure model performance against it. A session in one of our facilities can become part of what teaches a device to recognize a gesture, follow a gaze, or anchor a hologram.
If you are energized by hands-on operational work, prototype hardware, and AI-driven workflows, this role is for you. You will be responsible for translating engineering and research requirements into repeatable collection protocols, deploying the automation and AI tooling that scales them, and owning and collaborating with the hardware, tooling, and quality teams that make each collection reproducible.

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

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 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