Research Scientist Intern, Modern Recommendation Systems (PhD) Responsibilities
Initiate and lead efforts towards long-term ambitious research goals, while identifying intermediate milestones in the area of recommendation systems and models, user and content understanding and multi-modal (video, audio, and text) LLM analysis for classification and relevance use cases
Conduct original research that can eventually be applied to Meta product development, engage with the wider research community, including publishing and releasing open source software where appropriate
Design, train and support video understanding libraries and models to implement new features and functionality for use internally at Meta
Collaborate with researchers and cross-functional partners including communicating research plans, progress, and results.
Minimum Qualifications
Currently has or is in the process of obtaining a Ph.D. degree in Computer Science, Computer Vision, Artificial Intelligence, or 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, with experience in machine learning libraries such as Pytorch
Familiarity with AI/ML modeling techniques (e.g., LLM, RAG, LSTM, GRU, Transformers) and/or its acceleration for large scale use cases
Experience building systems based on machine learning and/or deep learning methods
Preferred Qualifications
Intent to return to the degree program after the completion of the internship/co-op
Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as publications at leading workshops or conferences such as NeurIPS, ICLR, KDD, ICML, SIGIR, WSDM, RecSys, CIKM, CVPR, ECCV, ACL, EMNLP, ICASSP, or similar
Experience working and communicating cross functionally in a team environment
Prior research or project experience in sequence modeling, recommendation systems, or user modeling
Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub)
