Machine Learning Scientist (Biomolecular Structure Prediction)

Location: Emeryville, California, United States. Legal authorization to work in the United States is required.

Salary: $212,000–$339,000 USD annually

Company

hirify.global is a frontier AI lab for biology that builds foundation models for life’s molecules, with applications in medicine, agriculture, protein design, and genome editing.

What you will do

• Develop and train all-atom structure prediction and co-folding models for antibody–antigen and protein–nucleic acid complexes.
• Diagnose failure modes involving CDR-H3 and epitope placement, interface geometry, nucleic acid conformations, and confidence calibration.
• Design architecture, training, and data interventions to improve model performance.
• Curate large-scale datasets from structural databases, distillation sets, and experimental binding and function data.
• Integrate structure models with generative models for design and filtering, collaborating with protein design teams on experimental validation.
• Present research results in regular update meetings.

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, 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 have

• 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.

Culture & Benefits

• High-growth opportunity focused on protein design and AI-driven biology.
• Competitive compensation with equity participation.
• 401(k) with a strong employer match.
• Health, dental, and vision insurance.
• Generous paid time off and a commitment to work-life balance.
• Professional development in an AI and biology field.