Research Scientist Intern, Feed Recommendations (PhD)

Meta; Bellevue (WA); On-site; USD 52,000 - 62,000; Part time

Job summary

Meta seeks Research Interns to join the Feed Recommendation team, tackling practical ML challenges at scale. You will frame real problems, develop innovative solutions, and work with researchers and engineers to advance recommender systems.

Internship durations range 12–24 weeks with multiple start dates. Ideal candidates are pursuing a PhD in CS/AI/NLP, have experience with Python/C++, and familiarity with PyTorch or TensorFlow.

Qualifications

• Pursuing or holding a PhD in CS/AI/NLP/related field.
• Work authorization for country of employment at hire and during internship.
• Proficient in Python, C++, Java or similar languages.
• Experience with PyTorch or TensorFlow.
• Experience building ML/NLP systems.
• Knowledge of transformers and large language models.
• Publications or grants in AI conferences desirable.
• Research or project experience in RL, LLM pre-training, diffusion models in rec systems.
• Software development experience via internships or OSS.
• Ability to work cross-functionally in fast-paced teams.
• Identify research opportunities and iterate with data-driven analysis.

Responsibilities

• Frame practical, high-impact challenges in recommendation into rigorous ML problems.
• Contribute to building innovative ML solutions under guidance from researchers and engineers.
• Collaborate with research scientists and software engineers on projects.
• Work on generative models and deep learning architectures relevant to recommender systems.

Skills

Python

C++

Deep learning

Transformers

Research experience

Cross-functional collaboration

ML/NLP projects

Education

Ph.D. in CS/AI/NLP/related field

Tools

PyTorch

TensorFlow

Git

Linux

Job description

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.

• 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.)
• 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