Research Scientist Intern, Video Recommendations (PhD)

This team is directly in charge of recommending all sorts of videos to users. In this role, you'll be working primarily with Facebook's video content, which is predominantly driven by our recommendation system. Your job will involve optimizing our system to provide users with the most engaging and high-quality content. Our interns will have the opportunity to participate in the process of framing practical, high-impact challenges in recommendation into rigorous machine learning problems, and bring their own expertise to build innovative solutions under the guidance from our Research Scientists and Software Engineers.

More concretely, you will focus on solving end-to-end problems in the intersection of Product, ML and Infra, and serve as a bridge between high-demand business needs and long-term foundational investments via ML approach.

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

Responsibilities

Perform research to advance the science and technology of intelligent machines. Develop novel and accurate NLP algorithms and systems, leveraging Deep Learning and Machine Learning on big data resources. Analyze and improve efficiency, scalability, and stability of various deployed systems. Collaborate with researchers and cross-functional partners including communicating research plans, progress, and results. Publish research results and contribute to research that can be applied to Meta product development.

Qualifications

Currently has or is in the process of obtaining a Ph.D. degree in Computer Science, Artificial Intelligence, Natural Language Processing, Speech Recognition, Sentiment Analysis, Computer Vision, or relevant technical field 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 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, ACL, EMNLP and KDD Prior research or project experience in one or more of the following areas: reinforcement learning, pre-training and supervised fine-tuning of large language models, sequence modeling, diffusion models, and their applications in recommendation 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