Data Scientist

Meta; San Francisco (CA); On-site; USD 177,000 - 247,000; Full time

Job summary

Meta is seeking a Data Scientist to join the Safety team within Meta Superintelligence Labs (MSL). The role focuses on creating measurement and mitigation systems for policy violations and emergent harms, with responsibilities spanning model evaluation, experimentation, and real-time monitoring infrastructure.

The position emphasizes building statistical telemetry, scalable evaluation for evolving agentic models, and assessing safeguards through offline and online evaluation, while developing

Qualifications

• Bachelor’s degree in CS/CE/Math/Stats (or equivalent practical experience).
• Minimum 6 years of work experience in analytics (4 years with a Ph.D.).
• Experience with data querying languages such as SQL, scripting languages such as Python, and/or statistical or mathematical software such as R.

Responsibilities

• Measure abuse and harms: develop statistical telemetry and measurement frameworks to detect, monitor, and understand policy violations or emergent harms.
• Scale model evaluation: create a scalable evaluation framework that adapts to changing agentic model capabilities and the safety or risk landscape, grounded in statistics.
• Evaluate safeguards: scale offline and online performance evaluation of safeguards using human-in-the-loop methods and active learning.
• Design experiments and analyses: run controlled experiments and rollouts to measure the impact of policy or risk definition changes and safety mitigations.
• Build reporting and intelligence: develop signals, pipelines, dashboards, and real-time monitoring mechanisms to support interventions and safety planning.

Education

Bachelor’s degree in Computer Science / Computer Engineering / Mathematics / Statistics

Master’s or Ph.D. degree in a quantitative field

Tools

SQL

Python

R

Job description

Meta is seeking a Data Scientist to join the Safety team within Meta Superintelligence Labs (MSL). The role focuses on creating measurement and mitigation systems for policy violations and emergent harms, with responsibilities spanning model evaluation, experimentation, and real-time monitoring infrastructure.

Role Overview

The position centers on building statistical telemetry and measurement frameworks, designing scalable evaluation approaches for evolving agentic model capabilities, and assessing the effectiveness of safeguards through both offline and online evaluation. You will also develop pipelines, dashboards, and monitoring signals that support interventions and inform safety roadmaps.

Key Responsibilities

• Measure abuse and harms: develop statistical telemetry and measurement frameworks to detect, monitor, and understand policy violations or emergent harms.
• Scale model evaluation: create a scalable evaluation framework that adapts to changing agentic model capabilities and the safety or risk landscape, grounded in statistics.
• Evaluate safeguards: scale offline and online performance evaluation of safeguards using human-in-the-loop methods and active learning.
• Design experiments and analyses: run controlled experiments and rollouts to measure the impact of policy or risk definition changes and safety mitigations.
• Build reporting and intelligence: develop signals, pipelines, dashboards, and real-time monitoring mechanisms to support interventions and safety planning.

Required Qualifications

• Bachelor’s degree in Computer Science, Computer Engineering, Mathematics, or Statistics (or equivalent practical experience).
• Minimum 6 years of work experience in analytics (minimum 4 years with a Ph.D.).
• Experience with data querying languages such as SQL, scripting languages such as Python, and/or statistical or mathematical software such as R.

Preferred Qualifications

• Experience in frontier AI products or risks, including navigating online adversarial environments in Trust & Safety or Fraud/Risk/Security contexts, including model evals, threat modeling, actor telemetry, and human-in-the-loop review systems.
• Comfort working in fast-paced, high-ambiguity, cross-functional environments, including explaining risk dimensions clearly to policy stakeholders.
• Background working with ambiguous and sparse data to operationalize harm prevalence measurement, causal inference, and root-cause analysis, supported by strong quantitative and statistical foundations.
• Master’s or Ph.D. degree in a quantitative field.
• Experience implementing responsible and ethical AI practices such as risk assessment, bias mitigation, and quality and accuracy reviews.
• Demonstrated ability to integrate AI tools to optimize or redesign workflows and deliver measurable impact, such as efficiency or quality improvements.
• Demonstrated ongoing AI skill development, including prompt/context engineering and agent orchestration, and staying current with emerging AI technologies.

Technologies

• SQL
• Python
• R

Location and Compensation

Location: San Francisco, CA (onsite).
Salary: USD 177,000 - 247,000 per year.