Meta is seeking a highly specialized, research-driven expert to define the future of our Computer Use Agent (CUA) data creation. Moving fast to train the next generation of AI and product experiences requires foundational data that is precise, statistically sound, and unbiased.
In this role, you won't just analyze data, you will architect the scientific frameworks, task-creation methodologies, and quality standards that govern it. You will sit upstream of production, conducting primary and empirical research to establish the definitive "gold standard" protocols that engineering and product teams rely on. If you are passionate about data governance as a research discipline and want to impact the experience of billions of people, join us.
Responsibilities
- Establish Foundational Standards: Lead primary and secondary research to design, build, and implement rigorous, scalable CUA data quality standards and validation frameworks with Meta Superintelligence Labs.
- Develop Task Methodologies: Architect scientifically sound task creation methodologies and annotation guidelines to ensure downstream datasets are highly accurate, reproducible, and representative.
- Mitigate Data Risk: Conduct deep dive data integrity research to identify systemic biases or quality gaps, proactively mitigating "garbage in, garbage out" risks for AI model training.
- Cross Functional Leadership: Partner closely with data science, software engineering, and operational teams to translate complex research methodologies into clear, executable data pipelines.
- Define Metrics & Baselines: Establish statistical baselines and key quality metrics to evaluate, audit, and continuously improve CUA data health across the product lifecycle.
Minimum Qualifications
- Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta
- Currently has, or is in the process of obtaining a Master’s or PhD degree in a quantitative or research-heavy field (e.g., Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience)
- Demonstrated experience conducting empirical research, data curation & validation, or designing complex data collection/annotation methodologies
- Deep expertise in establishing data quality control frameworks, statistical sampling, and data governance
- Must obtain work authorization in country of employment at the time of hire and maintain ongoing work authorization during employment PhD in a highly quantitative or empirical research field (e.g., Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience)
- Experience designing and scaling data quality frameworks or human-in-the-loop (HITL) annotation methodologies specifically for training Machine Learning, NLP, LLM foundation models
- Familiarity with user behavior log data, or defining complex interaction taxonomies
- Experience solving complex problems and evaluating alternative solutions, tradeoffs, and perspectives to determine a path forward
- Proven track record of building data governance metrics and quality baselines from scratch in an ambiguous, fast-paced environment
- Experience translating complex, abstract research methodologies into clear, executable operational pipelines for cross-functional engineering and product partners