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Visiting Researcher, Artificial Intelligence for Biology (University Grad)
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New York, NY
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The FAIR Protein team does fundamental research to help shape the future of AI in biology.
Central to this is using state-of-the-art AI to learn biology in its native language. This encompasses training large language models on protein sequence data, building research frameworks in protein ML, leveraging those techniques for protein design, and all aspects of conducting exploratory research in collaboration with FAIR’s research scientists and various academic collaborators at universities. Candidates are expected to be graduate or postgraduate level researchers capable of using machine learning and deep learning techniques to model and design proteins. The position is for a full-time visiting researcher position for a one-year period. The ideal candidate has a deep background in both machine learning and protein sequences and structure.
Visiting Researcher, Artificial Intelligence for Biology (University Grad) Responsibilities
- Analysis of protein language models using biology domain expertise.
- Develop datasets for biological case studies on specific protein families.
- Developing and evaluating new language modeling paradigms to learn from protein sequence data.
- Develop ML models at the intersection of language modeling and protein structure prediction/folding.
Minimum Qualifications
- Currently has, or is in the process of obtaining, a Masters or PhD degree in Computer Science, Machine Learning, Computational Biology, or related field.
- Experience in Python, Lua, C++, C, C#, Java or similar language.
- Experience in Machine Learning.
- Research experience in computational biology, protein engineering, computational protein design, or systems biology.
- Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment.
Preferred Qualifications
- Research and software engineering experience in an academic or industrial setting demonstrated via an internship, work experience, or coding competitions, or open-source contributions.
- Experience with ML applied to biology, specifically language models or deep learning for protein structure prediction.
- Research track record in computational biology, protein engineering, computational protein design, or systems biology.
- Experience in ML frameworks such as PyTorch, Caffe2, TensorFlow, and Keras.
- Experience building systems based on machine learning and/or deep learning methods.
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