Research Scientist Intern, Search (PhD)

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.

Responsibilities

Advance generative modeling, retrieval, ranking, and personalization across query suggestions and results.
Develop models that use queries, user history, and multimodal content to generate useful search suggestions or retrieve and rank relevant content.
Develop training and evaluation methods using human judgments, LLM judges, and online experiments to improve quality, downstream engagement, and sustained search habits.
Analyze and improve the efficiency, latency, scalability, and stability of models in production serving.
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, Machine Learning, Natural Language Processing, Information Retrieval, 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
Experience building systems based on machine learning, deep learning methods, information retrieval, or natural language processing
Familiarity with algorithms behind generative model training and common deep learning architectures (Transformers, GPTs, BERT etc.) 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 conferences such as NeurIPS, ICML, ICLR, ACL, EMNLP, KDD, SIGIR, WSDM, WWW, RecSys, or CIKM
Prior research or project experience in one or more of the following areas: generative retrieval, learned content identifiers or vector quantization, dense retrieval, learning to rank, sequence modeling, pre-training and fine-tuning of large language models, reinforcement learning, multimodal understanding, query generation, personalized recommendation, preference optimization, long-term user behavior modeling, and their applications in search or 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