Research Intern, AI Systems and 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 is seeking Research Interns to join Monetization Ranking & AI Foundations where we strive to serve the best personalized ads to people, maximizing their personal utility and advertiser value.

We are committed to making fundamental advances in machine learning systems, core algorithmic and system hardware-software co-design, and infrastructures that enable and advance AI technologies.

Our interns closely collaborate with researchers and engineers within the organization and across the company (e.g., FAIR), push the boundaries of state-of-the-art research in ML systems and infrastructures, share the latest findings through top-tier publications and open source, and integrate innovations at an unprecedented scale.

Our internships are twelve (12) to sixteen (16), or twenty-four (24) weeks long and we have various start dates throughout the year.

Qualifications: Currently has or is in the process of obtaining a Ph.D.

degree in Machine Learning, Systems, Artificial Intelligence, Computer Science, or related fields Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment Experience with Python, C++, C, or other related languages Experience in real-system implementations (e.g., GPU, NPU) Experience building systems based on machine learning and/or deep learning methods Intent to return to the degree program after the completion of the internship/co-op Proven track record of achieving significant research results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as NeurIPS, ICML, ICLR, MLSys, ISCA, ASPLOS, CGO, PLDI, PACT, HPCA, MICRO, or similar Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g.

GitHub) Demonstrated experience and self-driven motivation in solving analytical problems using quantitative approaches Experience working and communicating cross functionally in a team environment Responsibilities: Conduct state-of-the-art research to advance the science and technology of Machine Learning Systems and Infrastructures in various technology areas, e.g., ranking and retrieval systems, distributed training/inference optimizations.

Contribute to research that leads to innovations in scalable machine learning systems, resource-efficient AI and neural network architectures for data and algorithm scaling, memory/communication and energy-efficient AI systems, and hardware/software co-design, AI-driven compiler, system design and performance optimization, etc.

Analyze and improve efficiency, scalability, and stability of corresponding deployed algorithms.

Collaborate with researchers and engineers across varied disciplines, including communicating research plans, progress, and results.

Publish research results and contribute to research that can be applied to Meta product development.