Research Scientist Intern, Search (PhD)

Job description

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 our Search AI teams within Facebook. Facebook Search helps people find the people, groups, events, videos and posts they are looking for, across one of the largest and fastest-changing content collections in the world. Our teams build AI that helps people decide what to search for and find useful results. This spans personalized query suggestions before and during a search, suggested queries on Reels, and retrieval and ranking of relevant content. We bring together generative AI, multimodal understanding, and personalization to help people express intent, act on curiosity, and discover useful content at Facebook scale. Our interns will have the opportunity to frame practical, high-impact search problems as rigorous machine learning problems, and bring their own expertise to build innovative solutions under the guidance of our research scientists and software engineers. The central research challenge is to connect what a person wants to explore with content that makes the journey worthwhile. For example, a video can spark curiosity before someone has a query in mind; the system must suggest a useful question and deliver results that reward that curiosity. Models must understand explicit queries or infer interests from multimodal content, generate useful suggestions or retrieve relevant results, and balance engagement with relevance, groundedness, and factuality within strict latency and compute constraints. Open problems include generative retrieval using learned identifiers that represent content, personalized query generation, sequence models for ranking individual results and composing a useful results page, and joint learning across suggestions and results. LLM-based supervision, post-training, and online experiments help us connect model improvements to useful exploration and sustained search habits. If you are excited about helping billions of people turn curiosity into useful discovery, from the first search suggestion to the results that follow, come join us! Our internships are twelve (12) to sixteen (16), or twenty-four (24) weeks long and we have various start dates throughout the year.