The mission of Meta FAIR's Brain and AI is to interface neuroscience and artificial intelligence, to both better understand how the brain functions in light of state-of-the-art AI models and to improve AI in light of neuroscientific principles. We explore, design, and build AI systems at scale to enable next-generation AI technologies.Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year.

Research Scientist Intern, Neuroscience / AI (PhD) Responsibilities:

• Perform research to advance the science and technology of intelligent machines.
• Develop novel and accurate AI algorithms and systems to process neural data, leveraging Deep Learning and Machine Learning on big data resources.
• Analyze and improve efficiency, scalability, and stability of various deployed systems.
• 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.

• Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment.
• Experience with Python.
• Experience with deep learning frameworks such as Pytorch.
• Experience building systems based on machine learning, deep learning methods, or natural language processing.
• Experience in high-performance computing.
• Experience in collaborative coding (Github)

• Intent to return to a PhD 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, ICLR, AAAI, RecSys, KDD, IJCAI, CVPR, ECCV, ACL, NAACL, EACL, ICASSP, or similar.
• Experience with ML areas such as NeuroAI, Speech, Natural Language Processing, Multimodal Reasoning & Retrieval.
• Experience manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources.
• Experience with training deep neural networks for processing neural signals and multivariate time series.
• Experience with the development of enterprise level AI, Machine Learning, and Deep Learning platforms involving big data management and GPU compute.
• 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.