Research Scientist Intern, AI Core Machine Learning (PhD)

Meta; Sunnyvale, Bellevue, Seattle (CA, WA, WA); On-site; USD 41,000 - 69,000; Part time

Job summary

Meta in Sunnyvale, CA seeks a Research Intern to contribute to Monetization Ranking & AI Foundations, focusing on advancing AI/ML for ads ranking, retrieval, and modeling at scale.

You'll collaborate with researchers and engineers across teams, work on deep learning systems, and share insights through publications and open source in a fast-growing environment.

This internship ranges from 12 to 24 weeks with multiple start dates throughout the year.

Qualifications

• Pursuing or has PhD in ML/AI, CS, or related fields.
• Work authorization to be eligible in the country of employment.
• Experience with deep learning research and building systems.
• Strong track record of research results in top venues (grants, papers).

Responsibilities

• Collaborate with researchers and engineers to push state-of-the-art ML/AI.
• Contribute to publications and open-source projects.
• Explore applications in ranking, retrieval, and modeling.
• Build and evaluate ML systems at scale.

Skills

DL frameworks

Programming languages

ML research

Large-scale data

Cross-functional

Open source

Education

Ph.D. in ML/AI/CS

Tools

PyTorch

TensorFlow

Git

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 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 artificial intelligence and machine learning technologies that can be applied to all aspects of Monetization, including but not limited to, ranking, retrieval, supply, model architecture, representation learning, optimization, and generally a deep understanding of the users, ads, and any signals and contents related to their interactions.

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 AI and ML, 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.

• Currently has or is in the process of obtaining a Ph.D. degree in Machine Learning, Artificial Intelligence, Computer Science, Information or Multimedia Retrieval, Computer Vision, Natural Language Processing, Reinforcement Learning, Optimization, Computational Statistics, Applied Mathematics, or related technical fields
• Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment
• Experience with deep learning frameworks such as Pytorch or Tensorflow
• Experience with Python, C++, C, Java, or other related languages
• Experience with research and building systems based on machine learning and/or deep learning methods
• Intent to return to 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, AAAI, KDD, IJCAI, CVPR, ICCV, ACL, NAACL, ICASSP, 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 manipulating and analyzing complex, high‑volume, high‑dimensionality data from varying sources for large‑scale training
• Experience working and communicating cross functionally in a team environment