Research Scientist Intern - Scam and Trust

New Grad; Menlo Park (CA); On-site; USD 28,000 - 41,000; Full time

Job summary

Meta is seeking Research Scientist Interns to join the Scam and Trust team within Facebook. Interns will develop novel modeling and evaluation methods to detect and prevent scams affecting billions of users, collaborating with researchers and engineers.

Internships last 12 to 16 weeks or 24 weeks with multiple start dates; opportunities include combining LLM reasoning with actor representations, gathering evidence, and evaluating robustness against adversaries.

Qualifications

• Experience with ML concepts and evaluation metrics.
• Ability to work with noisy real-world data and signals.
• Strong communication of research findings.

Responsibilities

• Collaborate with researchers to translate scam detection challenges into a research project.
• Develop modeling approaches and evaluate them on realistic data.
• Explore LLM reasoning with actor representations and signals.
• Prepare results for publication and production validation.

Skills

Machine learning

Python

Research methods

Data analysis

Education

Pursuing CS/Math/ML degree

Tools

PyTorch

TensorFlow

Scikit-learn

Job description

Meta was created to foster connection and sharing worldwide. Over the past decade, our platforms have transformed global communication. With over a billion active users, joining Meta provides extraordinary opportunities to make a meaningful impact within a rapidly evolving organization.

Meta is seeking Research Scientist Interns to join our Scam and Trust team within Facebook. Scams are a global financial threat. At Meta’s scale, proactive detection and prevention are essential for protecting billions of users worldwide, making addressing scams one of the top priorities at Meta. The space is highly adversarial as bad actors continually change tactics and operate across content, behavior, and social networks, making isolated signals and static models insufficient. You will develop novel modeling and evaluation methods that address these challenges.

You will work alongside research scientists and engineers to translate a real-world scam-detection and prevention challenge into a focused research project. Research opportunities include:

• combining large language model (LLM) reasoning with actor representations and content or behavioral signals
• developing agentic systems that use tools to gather evidence, investigate suspicious activity, and adapt their reasoning

You may also investigate recursive self-improvement methods that enable LLM-based systems to identify weaknesses, propose improvements to their strategies or workflows, and validate those improvements through rigorous evaluation. Other research opportunities include learning from noisy and limited labels, transferring methods across surfaces, and maintaining robustness under distribution shift as adversaries evolve. Promising research may inform production systems that protect people from scams and contribute to research publications.

Our internships are twelve (12) to sixteen (16), or twenty-four (24) weeks long and we have various start dates throughout the year.