Research Scientist Intern, Alignment - Brain and AI (PhD) (Paris) Responsibilities
Develop novel state-of-the-art reinforcement learning, natural language and computer vision algorithms and corresponding systems, leveraging various deep learning techniques.
Analyze and improve efficiency, scalability, and stability of pretraining, encoding and decoding models
Perform state of the art research to advance the science and technology of Machine Learning and Artificial Intelligence applied to neural time series.
Devise better data-driven models for information retrieval, Multi-modal fusion, generation or media understanding (CV, NLP, Speech/ Audio).
Collaborate with researchers and cross-functional partners including communicating research plans, progress, and results.
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, Reinforcement Learning, Mathematics and neuroscience[additional], 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 language
Experience with deep learning frameworks such as Pytorch or Tensorflow
Experience building systems based on machine learning and/or deep learning methods
Research experience with algorithms for sequential decision-making, e.g., planning, reinforcement learning, or similar
Preferred Qualifications
Intent to return to degree program after the completion of the internship/co-op
Experience in encoding, decoding or pretraining of large scale algorithms applied to neural time series (functional imaging or electrophysiology)
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, ICLR, AAAI, RecSys, KDD, IJCAI, CVPR, ECCV, ACL, NAACL, EACL, ICASSP, or similar
Demonstrated experience and self-driven motivation in solving analytical problems using quantitative approaches
ML/ AI research and/ or work experience in information retrieval problems, generative approaches, and/ or Natural Language Processing, CV, or Speech/ Audio
Experience building systems based on machine learning, reinforcement learning and/or deep learning methods
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
