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

Meta; Bellevue, Seattle (WA, WA); Sur place; USD 55 000 - 83 000; Plein temps

Résumé du poste

Meta is seeking Research Interns in the Integrity (Trust and Safety) team. You will work on applied ML to detect harm from bad actors, spanning agentic AI systems and scalable data-driven solutions.

The role involves framing research problems, conducting experiments, and contributing to publications under senior researchers. The internship emphasizes end-to-end ML workflows with tools like Gemini/Claude/Muse, and collaboration across ML engineers and scientists to push safety and detection

Qualifications

• Currently pursuing a PhD or in progress in Computer Science, AI, ML, NLP, CV, Statistics, or related technical field.
• Experience with Python and deep learning frameworks such as PyTorch.
• Familiarity with large language models (LLMs) and AI-native development.

Responsabilités

• Intern will own one focused research problem end-to-end from framing to experiments to publication.
• Work under guidance of research scientists and ML engineers to develop and deploy AI-driven agentic workflows.

Connaissances

Python

PyTorch

Deep learning

LLMs

ML research

NLP

Experiment design

Data analysis

Deployment

Git

Formation

PhD candidate in CS/AI/ML/NLP

Outils

PyTorch

Git

Description du poste

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.

Currently has or is in the process of obtaining a Ph.D. degree in Computer Science, Artificial Intelligence, Machine Learning, Natural Language Processing, Computer Vision, Statistics, or relevant technical fieldMust obtain work authorization in country of employment at the time of hire and maintain ongoing work authorization during employmentExperience with PythonExperience with deep learning frameworks such as PyTorchResearch experience in machine learning, including training or fine-tuning deep learning models and designing experiments to evaluate themFamiliarity with large language models and AI-native development (Claude, Codex etc) Intent to return to the degree program after the completion of the internship/co‑opProven track record of research achievements as demonstrated by publications at leading venues such as NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, COLM, CoLLAs, KDD, WWW or ICWSMResearch or project experience in one or more of: continual or lifelong learning, multi-task learning and model merging, active learning, knowledge distillation, LLM post-training (SFT, preference optimization, RL), uncertainty estimation and calibration, learning from AI feedback or LLM judges, multimodal modeling, or sequence/graph modeling of user behaviorExperience applying ML to trust and safety, content moderation, fraud or abuse detectionExperience with large-scale or parameter-efficient training (e.g., LoRA, distributed training)Demonstrated software development experience via internships, work experience, coding competitions, or widely used open-source contributionsExperience working and communicating cross-functionally in a fast-paced environment, with the ability to frame real-world problems as rigorous ML problems and drive iterations from clear analysis