Research Scientist Intern, Generative AI Algorithms (PhD)
Meta

Job Info

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.We are committed to advancing the field of Generative AI by making fundamental advances in technologies to help interact with our world and empower content creation. We are seeking individuals passionate in areas such as retrieval augmented generation (RAG). Our interns have an opportunity to make core algorithmic advances and apply their ideas at an unprecedented scale.Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year.

Research Scientist Intern, Generative AI Algorithms (PhD) Responsibilities:

• Develop novel state-of-the-art retrieval augmented generation (RAG) algorithms and corresponding systems, leveraging various deep learning techniques
• Based on the project, help analyze and improve efficiency, scalability, and stability of corresponding deployed algorithms
• Perform research to advance the science and technology of intelligent machines
• Perform research that enables learning the semantics of data (images, video, text, audio, and other modalities)
• 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 Machine Learning or Artificial Intelligence, 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 or other related language
• Experience building systems based on machine learning and/or deep learning methods

Preferred Qualifications:

• Intent to return to degree program after the completion of the internship/co-op
• Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, EMNLP or similar
• Experience working and communicating cross functionally in a team environment
• Experience in advancing AI techniques in retrieval augmented generation (RAG), large language model (LLM), including core contributions to open source libraries and frameworks in RAG
• Experience solving analytical problems using quantitative approaches
• Experience setting up ML experiments and analyze their results
• Experience manipulating and analyzing complex, large scale, high-dimensionality data from varying sources
• Experience in utilizing theoretical and empirical research to solve problems
• Experience with deep learning frameworks

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