Research Scientist Intern - Multiple Teams

New Grad; Bellevue (WA); On-site; USD 55,000 - 83,000; Full time

Job summary

Meta is seeking Research Interns to join the Integrity (Trust and Safety) team. You will work on applied ML to detect harms from bad actors and supervise end-to-end ML workflows, including AI-driven agentic systems and deployment at scale.

You will frame research problems, run experiments, and contribute to publications under the guidance of research scientists and ML engineers, with exposure to Gemini/Claude/Muse as part of scalable data-driven solutions.

Responsibilities

• Interns work on a focused research problem from framing to experiments to publication under guidance of research scientists and ML engineers.
• You will own end-to-end ML workflows including AI-driven agentic processes and model deployment.

Job description

Meta 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 harms

Interns 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.