The next frontier is authoring new biology, and with AI, we have the power to write the future—creating solutions that transform medicine, agriculture, and beyond. Backed by Insight and Air Street.
About the role
We're looking for a motivated and creative Machine Learning (ML) Scientist to drive research into biomolecular structure prediction. You will build and improve all-atom co-folding models, with a focus on the complexes that matter most to our programs: antibody–antigen interfaces and protein–nucleic acid assemblies. These remain among the hardest cases for current models, and closing that gap directly accelerates our antibody design and genome editing work. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- PhD (or equivalent industry experience) in computer science, computational biology, biophysics, or a related field
- Hands-on experience building and training biomolecular structure prediction models (e.g., AlphaFold- or Protenix-style co-folding models) at a leading academic or industry lab
- Publications at major ML conferences (NeurIPS, ICML, ICLR) or top science journals, or widely used open-source models
- Experience with deep learning frameworks such as PyTorch or JAX, including multi-GPU training
More about this role
Profluent is the frontier AI lab for biology. Profluent builds powerful foundation models for all of life's molecules, unlocking solutions that transform medicine, agriculture, and beyond. Founded in 2022 and headquartered in Emeryville, CA, Profluent is backed by leading investors including Altimeter Capital, Bezos Expeditions, Spark Capital, Insight Partners, Air Street Capital, AIX Ventures, and Convergent Ventures and has raised over $150M to date.
We're looking for a motivated and creative Machine Learning (ML) Scientist to drive research into biomolecular structure prediction. You will build and improve all-atom co-folding models, with a focus on the complexes that matter most to our programs: antibody–antigen interfaces and protein–nucleic acid assemblies. These remain among the hardest cases for current models, and closing that gap directly accelerates our antibody design and genome editing work.
The ideal candidate has trained structure prediction models at a leading academic or industry lab and knows where today's models break. You will be a self-directed researcher who moves quickly from hypothesis to trained model to rigorous evaluation. As an early member of the ML...
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