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Research Scientist Intern, Neuroscience / AI
Research Scientist Intern, Neuroscience / AI-April 2024
London
Apr 15, 2025
ABOUT META
We’re building a team as diverse as the communities and billions of people we serve every day. Our teammates don’t need to conform here. Lived experiences are an asset, and we value your unique perspe
10,000+ employees
Social Media, Technology
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About Research Scientist Intern, Neuroscience / AI

  Summary:

  Meta was built to help people connect and share, and over the last decade our tools have played a critical part in changing how people around the world communicate with one another. With over a billion people using the service and more than fifty offices around the globe, a career at Meta offers countless ways to make an impact in a fast growing organization.We are committed to advancing the field of artificial intelligence by making fundamental advances in technologies to help interact with and understand our world. We are seeking individuals passionate in areas such as deep learning, computer vision, audio and speech processing, natural language processing, machine learning, reinforcement learning, computational statistics, and applied mathematics. Our interns have an opportunity to make core algorithmic advances and apply their ideas at an unprecedented scale.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.

  Required Skills:

  Research Scientist Intern, Neuroscience / AI 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.

  Minimum Qualifications:

  Minimum Qualifications:

  Currently has or is in the process of obtaining a Masters degree in Computer Science, Artificial Intelligence, Neuroscience, Speech Recognition, Natural Language Processing, 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.

  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)

  Preferred Qualifications:

  Preferred Qualifications:

  Intent to return to 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 platform 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.

  Industry: Internet

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