Research Scientist Intern, Search (PhD)

Meta•Menlo Park, CA

1d•$7,650 - $12,667

About The Position

Meta is seeking Research Interns to join its Search AI teams within Facebook. Facebook Search helps people find people, groups, events, videos, and posts across a large and rapidly changing content collection. The teams build AI for query suggestions, retrieval, and ranking, using generative AI, multimodal understanding, and personalization. Interns will frame practical, high-impact search problems as rigorous machine-learning problems and develop solutions with guidance from research scientists and software engineers. The research focuses on connecting users’ interests and queries with useful content, including generating search suggestions, retrieving relevant results, and ranking or composing results pages. Work may address generative retrieval, personalized query generation, sequence models, joint learning across suggestions and results, LLM-based supervision and post-training, and online experiments. Models must balance engagement with relevance, groundedness, factuality, latency, and compute constraints. Internships are 12 to 16 weeks, or 24 weeks, with various start dates throughout the year.

Requirements

Currently have or be in the process of obtaining a Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Natural Language Processing, Information Retrieval, or a relevant technical field.
Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment.
Experience with Python, C++, C, Java, or another related language.
Experience with deep-learning frameworks such as PyTorch or TensorFlow.
Experience building systems based on machine-learning or deep-learning methods, information retrieval, or natural language processing.
Familiarity with algorithms behind generative-model training and common deep-learning architectures, such as Transformers, GPTs, and BERT.

Nice To Haves

Intent to return to the degree program after completing the internship or co-op.
Research achievements demonstrated by grants, fellowships, patents, or publications at leading conferences such as NeurIPS, ICML, ICLR, ACL, EMNLP, KDD, SIGIR, WSDM, WWW, RecSys, or CIKM.
Research or project experience in areas such as generative retrieval, learned content identifiers or vector quantization, dense retrieval, learning to rank, sequence modeling, pre-training and fine-tuning large language models, reinforcement learning, multimodal understanding, query generation, personalized recommendation, preference optimization, long-term user behavior modeling, or applications in search or recommendation systems.
Software development experience through technical internships, work experience, coding competitions, or widely used contributions to open-source machine-learning repositories.
Experience working and communicating cross-functionally in a fast-paced team environment, including the ability to identify research opportunities in real-world applications, select appropriate machine-learning methods, and drive iterations based on analysis of results.

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, communicating research plans, progress, and results.
Publish research results and contribute to research applicable to Meta product development.