Machine Learning Scientist, Biomolecular Structure Prediction

Profluent • Emeryville, CA

1d • $212,000 - $339,000

About The Position

Profluent is seeking a motivated and creative Machine Learning Scientist to drive research into biomolecular structure prediction. The role will build and improve all-atom co-folding models, particularly for antibody–antigen interfaces and protein–nucleic acid assemblies, to support antibody design and genome editing programs. The successful candidate will be a self-directed researcher who moves quickly from research hypotheses to trained models and rigorous evaluation. As an early member of the ML team, the scientist will help shape how structure modeling works alongside Profluent's generative models and experimental platforms.

Requirements

• 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, such as AlphaFold- or Protenix-style co-folding models, at a leading academic or industry lab.
• Publications at major ML conferences such as NeurIPS, ICML, or ICLR, or in top science journals, or widely used open-source models.
• Experience with deep learning frameworks such as PyTorch or JAX, including multi-GPU training.

Nice To Haves

• Experience modeling antibody structure and antibody–antigen docking, including CDR loop modeling and epitope prediction.
• Experience modeling protein–DNA or protein–RNA complexes.
• Deep familiarity with structural data pipelines, including mmCIF processing, MSA and template search, structural clustering, and dataset splitting.
• Experience with diffusion or flow-matching models for structure generation or structure-conditioned design.
• Experience with large-scale distributed training and cloud compute.
• Familiarity with experimental structural and binding data, including cryo-EM, crystallography, and display-based binding assays.

Responsibilities

• Develop and train all-atom structure prediction models, improving accuracy on antibody–antigen and protein–nucleic acid complexes.
• Diagnose failure modes of current models, including CDR-H3 and epitope placement, interface geometry, nucleic acid conformations, and confidence calibration; design architecture, training, and data interventions to address them.
• Curate large-scale training data from structural databases, distillation sets, and Profluent's experimental binding and function data.
• Integrate structure models with generative models for design and filtering, and work with protein design teams on experimental validation.
• Present results to colleagues in regular update meetings.

Benefits

• Competitive compensation package with equity participation.
• 401(k) with a strong employer match.
• Comprehensive health, dental, and vision insurance.
• Generous PTO policy and commitment to work-life balance.
• Professional development opportunities in a cutting-edge field at the intersection of AI and biology.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree