Research Scientist Intern, Scam and Trust (PhD)

Meta•Menlo Park, CA

7d•$7,650 - $12,667

About The Position

Meta is seeking a Research Scientist Intern to join its Scam and Trust team within Facebook. The role focuses on developing novel modeling and evaluation methods to detect and prevent scams at Meta’s scale, where adversaries change tactics across content, behavior, and social networks. The intern will work with research scientists and engineers to translate a real-world scam-detection and prevention challenge into a focused research project. Research may involve combining large language model (LLM) reasoning with actor representations and content or behavioral signals; developing agentic systems that gather evidence, investigate suspicious activity, and adapt their reasoning; and exploring recursive self-improvement methods that identify weaknesses and validate proposed improvements through rigorous evaluation. Other topics include learning from noisy and limited labels, transferring methods across surfaces, and maintaining robustness as adversaries and data distributions change. Promising research may inform production systems that protect people from scams and contribute to research publications. Internships last 12–16 or 24 weeks, with various start dates throughout the year.

Requirements

Currently has or is in the process of obtaining a Ph.D. in Computer Science, Artificial Intelligence, Natural Language Processing, Speech Recognition, Sentiment Analysis, Computer Vision, or a relevant technical field.
Must obtain work authorization in the country of employment by the time of hire and maintain ongoing work authorization during employment.
Experience with deep learning frameworks.
Experience building systems based on machine learning, deep learning methods, or natural language processing.
Familiarity with algorithms behind generative model training and common deep learning architectures such as Transformers and BERT.

Nice To Haves

Intent to return to the degree program after completing the internship or co-op.
Research achievements demonstrated through grants, fellowships, patents, or publications at leading AI conferences such as NeurIPS, ICML, ICLR, ACL, EMNLP, and KDD.
Research or project experience in one or more of LLM reasoning, agentic systems and tool use, recursive self-improvement, reinforcement learning, automated evaluation, learning from feedback, behavioral or graph representations, weak supervision, or transfer learning; applying these methods to adversarial or other real-world problems is a plus.
Software development experience through technical internships, work experience, coding competitions, or widely used contributions to open-source machine-learning repositories.
Experience working and communicating cross-functionally in a fast-paced team environment, with the ability to identify research opportunities in real-world applications, select appropriate ML methods, and proactively drive iterations based on analysis of results.

Responsibilities

Formulate and investigate a focused research question in adversarial scam detection and prevention.
Develop LLM reasoning and representation-learning methods combining content, behavioral, or social-network evidence to identify scams and distinguish malicious activity from legitimate behavior.
Research agentic systems that plan investigations, use tools, and synthesize evidence, evaluating both individual decisions and end-to-end effectiveness.
Investigate recursive self-improvement and feedback-driven methods that improve agent strategies, reasoning, or workflows over successive iterations, using controlled evaluation to distinguish genuine gains from overfitting or error amplification.
Design rigorous experiments evaluating robustness under noisy labels and distribution shift, detection–false-positive trade-offs, and computational efficiency.
Collaborate with research scientists, engineers, and cross-functional partners to prototype promising approaches, assess production applicability, and communicate research plans, progress, and results.
Document research findings and, where appropriate, contribute to publications and research that can inform Meta product development.