Research Scientist Intern, Feed Recommendations (PhD)

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• Research Scientist Intern, Feed Recommendations (PhD)
• Bellevue, WA +1 location
• Artificial Intelligence
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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 Feed Recommendation team within Facebook. This org is directly in charge of recommending all sorts of contents to users the time they open the app, and thus always gets the first-hand experience of critical pain points to solve. Our interns will have the opportunity to participate in the process of framing practical, high-impact challenges in recommendation into rigorous machine learning problems, and bring their own expertise to build innovative solutions under the guidance from our research scientists and software engineers.

More concretely, while the generative AI innovations in NLP are mainly focusing on optimizing the probabilities of selecting the next word token, in recommendation the vocabulary (contents to suggest) is at a much larger scale and also changing at a fast speed. The preference for these contents can also vary dramatically across users and time. If you’re excited about how to generate the correct sequences of “words” any time the giant number of Facebook users open or scroll down the blue app, come to 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.

Research Scientist Intern, Feed Recommendations (PhD) Responsibilities

• Perform research to advance the science and technology of intelligent machines
• Develop novel and accurate NLP algorithms and systems, leveraging Deep Learning and Machine Learning on big data resources
• Analyze and improve efficiency, scalability, and stability of various deployed systems
• 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

Minimum Qualifications

• Currently has or is in the process of obtaining a Ph.D. degree in Computer Science, Artificial Intelligence, Natural Language Processing, Speech Recognition, Sentiment Analysis, Computer Vision, 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, or natural language processing
• Familiarity with algorithms behind generative model training and common deep learning architectures (Transformers, GPTs, BERT etc.)

Preferred Qualifications

• 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 AI conferences such as NeurIPS, ICML, ICLR, ACL, EMNLP and KDD
• Prior research or project experience in one or more of the following areas: reinforcement learning, pre-training and supervised fine-tuning of large language models, sequence modeling, diffusion models, and their applications in 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

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