About this role
Meta Fundamental AI Research (FAIR) is looking for Research Scientist Interns to advance the science of AI alignment and ML systems. Our teams work on making AI systems more reliable, efficient, more measurable, and better aligned with human intent- spanning reasoning and agents, evaluation and robustness, efficiency, safety and security, privacy, world modeling, and the societal dimensions of AI governance.
In this role you will embed with a world-class, multi-disciplinary team of research scientists and engineers to pursue an open research question end to end- from problem framing through experimentation to publication. Interns are expected to drive their own project with mentorship, not execute a predefined task list.
Applicants from a broad range of technical backgrounds are encouraged to apply, including machine learning, natural language processing, computer vision, reinforcement learning, statistics, and the social sciences as applied to AI systems. Our internships are twelve (12) to twenty four (24) weeks long, and we have various start dates throughout the year.
Responsibilities
Develop novel state-of-the-art algorithms and corresponding systems, leveraging various deep learning techniques
Analyze and improve various aspects of the corresponding algorithms and systems, including efficiency, scalability, stability, fairness, security, and privacy
Perform state of the art research to advance the science and technology of Machine Learning and Artificial Intelligence
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
Qualifications
Currently has or is in the process of obtaining a Ph.D. degree in Computer Science, Machine Learning, 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, C++, C, Java or other related languages
Experience with deep learning frameworks such as Pytorch or Tensorflow
First-author publication record at a top-tier ML or NLP or Systems venue (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, ICCV, MLSys) or equivalent 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 publications at leading workshops or conferences such as NeurIPS, ICLR, AAAI, RecSys, KDD, IJCAI, CVPR, ECCV, ACL, NAACL, EACL, ICASSP, CCS, IEEE S&P, MLSys, or similar
Demonstrated experience and self-driven motivation in solving analytical problems using quantitative approaches
Experience building large-scale machine learning systems and training with large datasets
Experience communicating complex research in a clear, precise, and actionable manner
Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub)
Experience working and communicating cross functionally in a team environment