Research Scientist Intern, AI Core Machine Learning (PhD) Responsibilities
Conduct state-of-the-art research to advance the science and technology of Machine Learning and Artificial Intelligence.
Develop novel algorithms and corresponding systems, leveraging various AI and ML techniques.
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.
Minimum Qualifications
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
Preferred Qualifications
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
