Research Scientist Intern, Modern Recommendation Systems - Machine Learning(PhD)
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 AI is seeking exceptional interns to join its User Understanding for Recommendations teams. These teams are a part of an AI Innovation Center for Recommendations, and collaborate closely with FAIR, other research teams (e.g. content understanding, core ML/ RL, etc.) as well as product teams for the Family of Apps (FB, IG, WhatsApp).
When you join our team, you are working to advance the state of the art in core disciplines such as: retrieval, ranking, clustering, search and multi-modal fusion problems, often in zero-shot/ few-shot settings, recommender systems, as well as graph learning. Together with product teams, we deploy these systems at scale to recommend the best, fresh and unconnected 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.
Area of Responsibilities
- Help us apply cutting-edge algorithms to a wide range of media understanding challenges at Meta’s Family of Apps.
- Perform state-of-the-art research to advance the science and technology of machine learning and artificial intelligence.
- Devise better data-driven models for information retrieval, recommender systems, and graph learning.
- Publish research results and contribute to research that can be applied to Facebook product development.
- Collaborate with researchers and engineers across diverse disciplines.
- Currently has, or is in the process of obtaining a PhD degree in Computer Science, Mathematics, Physics, or a related field
- Experience in Python, PyTorch, C++, Java or other related language
- Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment.
- Demonstrated experience and self-driven motivation in solving analytical problems using quantitative approaches.
- A strong interest in theoretical and empirical research and in answering hard questions with research.
- ML/ AI research and/ or work experience in information retrieval problems, recommender systems, and/ or graph learning.
- Ability to communicate complex research in a clear, precise, and actionable manner.
- Experience creating and optimizing large-scale systems based on machine learning (deep learning) methods.
- Intent to return to a degree-program after the completion of the internship/co-op.