Research Scientist Intern, AI Alignment

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