Data Scientist, Superintelligence Labs; Safety

We're seeking Data Scientists to join the Safety team within MSL (Meta Superintelligence Labs).As a Data Scientist in Safety, you will establish the analytical foundations that allow us to advance personal superintelligence safely and securely. You will help us turn complex, ambiguous risks with incomplete ground truth into measurable systems across model evaluations and online monitoring.

You'll work with engineering, research, product, policy, and legal to design and build measurement and mitigation strategies, quantifying trade-offs between user friction and safety risks.

Data Scientist, Meta Superintelligence Labs (Safety) Responsibilities:

Measure abuse: build statistical telemetry and measurement frameworks to detect and monitor policy violations or emergent harms

Scale model evaluation: design a scalable framework adapting to evolving agentic model capabilities and safety/risk landscape, grounded in statistics

Evaluate and optimize safeguards: scale offline and online performance evaluation of our safeguards using human-in-the-loop and active learning

Design experiments and analyses:
Conduct controlled experiments and rollouts to evaluate the impact of policy or risk definition changes and safety mitigations

Build intelligence and reporting: build signals, pipelines, dashboards to enable real-time monitoring driving interventions and safety roadmaps

Minimum Qualifications:

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience

Bachelor's degree in Mathematics, Statistics, Computer Science, Computer Engineering, relevant technical field, or equivalent practical experienceA minimum of 6 years of work experience in analytics (minimum of 4 years with a Ph.D.)Experience with data querying languages (e.g. SQL), scripting languages (e.g. Python), and/or statistical/mathematical software (e.g. R)

Preferred Qualifications:

Experience in frontier AI products or risks, and navigating online, adversarial environments in Trust & Safety or Fraud/Risk/Security domains. Model evals, threat modelling, actor telemetry, human-in-the-loop review systems don’t sound foreign to you Familiar with fast-paced, high-ambiguity, cross-functional environments – able to jump from agentic trace deep-dives to explaining risk dimensions in plain English to policy stakeholders

Background in ambiguous and sparse data environments to operationalize e.g. harm prevalence measurement, causal inference, root-cause analysis– rooted in strong quantitative/statistical foundations

Master's or Ph.D. Degree in a quantitative field

Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

About Meta:

Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and Whats App further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.

Meta is proud to be an Equal Employment Opportunity and…