Research Scientist Intern, Integrity (Trust and Safety) AI (PhD)

Meta Inc.; Menlo Park (CA); In loco; USD 83.000 - 103.000; Tempo pieno

Descrizione del lavoro

Meta seeks a Research Scientist Intern for the Integrity (Trust and Safety) AI team in Menlo Park, CA. You will tackle real-world harms using cutting-edge ML techniques and work under guidance from research scientists and ML engineers.

You will own an end-to-end research project—from framing to experiments to publication—plus E2E ML workflows with AI-driven agentic systems and scaled solutions via data analysis, feature engineering, and deployment.

Competenze

• PhD candidate in a relevant field.
• Experience framing and executing ML experiments.

Mansioni

• Own one focused research problem end to end—from framing to experiments to publication.
• Develop and deploy end-to-end ML workflows including data analysis, feature engineering and model deployment.

Formazione

PhD in a relevant field

Descrizione del lavoro

Research Scientist Intern, Integrity (Trust and Safety) AI (PhD)# Research Scientist Intern, Integrity (Trust and Safety) AI (PhD)Menlo Park, CA+1 locationResearch+ 2 moreApply nowMeta was built to help people connect and share, and over the last decade our tools have played a critical part in changing how people around the world communicate with one another. With over a billion people using the service and more than fifty offices around the globe, a career at Meta offers countless ways to make an impact in a fast growing organization.Meta is seeking Research Interns to join the Integrity (Trust and Safety) team within Facebook. We are the applied ML team developing cutting-edge AI solutions to detect harm from bad actors. Our models decide when a group spreading severe harm should be taken down, when a creator is faking engagement, when a page is impersonating someone, and when an enforcement should be reversed on appeal, across a platform used by billions of people.Our work has moved from per-post classifiers to advanced agentic AI systems that reason over an entire entity (its content, members, admins and history) and apply written policy the way an expert would, pushing the precision/recall boundaries on adversarial actor detection. The open research problems are about making that judgment efficient, reliable:-Pushing the performance boundaries for agentic AI applied to bad actor detection-Distilling large reasoning models into solutions that can scale cost-efficiently, including pushing the boundaries of decision-style models like Jev-Improving shared multi-harm model improving without regressing other harmsInterns own one focused research problem end to end, from framing to experiments to publication, under the guidance of our research scientists and machine learning engineers. You will own E2E ML workflows including AI-driven agentic workflows with Gemini/Claude/Muse as well as scaled solutions through data analysis, feature engineering, model development and deployment.