Research Scientist Intern, Modern Recommendation Systems (PhD)
Meta

Job Info

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 Core Ranking team within its "Modern Recommendation Systems" (MRS) AI Innovation Center. This org brings together a world-class R&D team of researchers, developers, and engineers. The MRS org is developing a unified infrastructure and model service to improve recommendations across Facebook, Instagram and WhatsApp, the "Family of Apps" (FoA). Our interns will have the opportunity to advance the state of the art in ranking models of recommendation systems. Together with product teams, we deploy these systems at scale to recommend the most relevant content to our users by improving existing approaches and incubating novel capabilities. The opportunities and challenges of this work are immense. Our work needs to demonstrate state of the art performance on shared tasks where available, while being applicable at Meta scale, so that we can serve our customers. Examples of our past work include Efficient Sequential User History Modeling, which is applied to multiple products by FB reels. Our internships are twelve (12) to sixteen (16), or twenty-four (24) weeks long and we have various start dates throughout the year.

Research Scientist Intern, Modern Recommendation Systems (PhD) Responsibilities:

• Conduct in-depth research in areas such as data mining, information retrieval, recommendation system, reinforcement learning, contextual bandit, optimization, model-system codesign, graph learning, sequence modeling, etc.
• Analyze and preprocess large-scale user behavior data to derive meaningful insights
• Collaborate with cross-functional teams to refine existing models and propose innovative approaches to improve the product's recommendation quality
• Document findings and present results to cross-functional teams to democratize the techniques

Minimum Qualifications:

• Currently has or is in the process of obtaining a Ph.D. degree in Computer Science or a related field.
• Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment.
• Proficiency in programming languages like Python, with experience in machine learning libraries such as TensorFlow and Pytorch.
• Familiarity with sequence modeling techniques (e.g., LSTM, GRU, Transformers)
• Excellent communication skills to collaborate with effectively with a diverse team and present findings.

Preferred Qualifications:

• Intent to return to the degree program after the completion of the internship/co-op.
• Prior research or project experience in sequence modeling, recommendation systems, or user modeling.
• 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.
• Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub).

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