Research Scientist Intern, Video RecSys (PhD)

Meta; Menlo Park (CA); On-site; USD 55,000 - 90,000; Full time

Benefits offered by this job

Paid internship

Mentorship by researchers

Stipend and perks

Job summary

Meta is seeking AI Research interns for the Video Recommendations team on Facebook. You will work on Recursive Self-Improvement to build agents that autonomously propose, run, and learn from improvements to video recommendation models.

Interns will frame high-impact challenges, develop end-to-end RSI prototypes, and collaborate with Research Scientists and Software Engineers to deliver evidence-based improvements. Internships last 12–24 weeks with multiple start dates.

Qualifications

• Pursuing PhD in CS/ML or related field with strong research track record.
• Experience with Python and ML frameworks (PyTorch, TensorFlow).
• Familiarity with RL/AutoML, large-scale experiments, and ML pipelines.

Responsibilities

• Develop an RSI prototype to propose, evaluate, and refine video recommendation improvements.
• Frame practical challenges into ML problems and run end-to-end experiments.
• Collaborate with Researchers and Software Engineers to deliver a working prototype.

Skills

Python

C/C++

Java

Deep learning

Neural networks

Experimentation

Cross-functional teamwork

Research communication

Education

PhD in CS/ML

Tools

PyTorch

TensorFlow

Job description

Meta is seeking AI Research interns to join our Video Recommendations team within Facebook. Videos (Reels, long videos, and Live streams) are increasingly the formats of choice across the social media industry, and making all these formats successful is a top priority for Facebook, and the core mission owned by this org. Time spent on Facebook watching videos accounts for 1.2+ Billion hours/day, which is over 56% of time spent globally on the App, and this number is expected to continue growing.

This team is directly in charge of recommending all sorts of videos to users, powered by our large-scale unified video recommendation models. In this role, you will work on Recursive Self-Improvement (RSI): building AI agents that autonomously propose, run, evaluate, and learn from improvements to these models, closing the loop between ML experimentation and model quality. Our interns will have the opportunity to frame practical, high-impact challenges in recommendation into rigorous machine learning problems, and bring their own expertise to build innovative solutions under the guidance of our Research Scientists and Software Engineers.

More concretely, you will develop an agent-driven RSI prototype that continuously proposes, evaluates, and refines improvements for a selected video recommendation task. The scope spans training data improvement, environment and experiment setup, automated offline model evaluation, and feedback collection from model quality signals such as normalized entropy (NE) and recall. You will use experiment outcomes to improve both the agent's intervention strategy and its evaluation criteria, and deliver a working end-to-end prototype with evidence against a fixed baseline. Work will be mentored by the team, with production changes and online experiments subject to owner review and approval.

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

• Currently has or is in the process of obtaining a Ph.D. degree in Computer Science, Artificial Intelligence, Machine Learning, or relevant technical field
• Must obtain work authorization in country of employment at the time of hire and maintain ongoing work authorization during employment
• Experience with Python, C++, C, Java or other related language
• Experience with deep learning frameworks such as Pytorch or Tensorflow
• Familiarity with machine learning and common deep learning architectures
• Intent to return to the degree program after the completion of the internship/co-op
• Proven track record of solid research achievements as demonstrated by grants, fellowships, patents, as well as publications at leading AI conferences such as NeurIPS, ICML, ICLR, KDD, RecSys, WWW, ACL and EMNLP
• Prior research or project experience in one or more of the following areas: LLM-based agents and agentic workflows, recursive self-improvement, automated machine learning (AutoML), data-centric AI (data selection, curation, and reweighting), reinforcement learning, and large-scale recommendation systems
• Experience building reproducible ML experimentation and evaluation pipelines on large-scale distributed training systems
• Demonstrated software development experience via tech internships, work experience, coding competitions, or widely used contributions in open source machine-learning repositories
• Experience working and communicating cross functionally in a fast-paced team environment

Ideal candidates should have the ability to quickly understand and identify the research opportunities behind real-world applications, select the appropriate ML methods to explore, and proactively drive the iterations based on clear analysis of the current results