ML Scientist: Biomolecular Structure Prediction & Design

Profluent Bio; Emeryville (CA); On-site; USD 212,000 - 339,000; Full time

Benefits offered by this job

Competitive compensation with equity

401(k) with strong employer match

Comprehensive health/dental/vision

Generous PTO and work-life balance

Professional development opportunities

Job summary

Profluent Bio seeks an ML Scientist to drive biomolecular structure prediction research. You will build all-atom co-folding models focused on antibody–antigen interfaces and protein–nucleic acid assemblies, integrating with Profluent’s generative and experimental platforms.

The role requires a PhD or equivalent experience, hands-on model building, and publications or open-source contributions. Join an early ML team shaping AI-driven biology.

Qualifications

• PhD (or equivalent) in CS, computational biology, biophysics or related field.
• Hands-on experience building and training biomolecular structure-prediction models.
• Publications at ML venues or top journals, or open-source model usage.
• Experience with deep learning frameworks and multi-GPU training.

Responsibilities

• Develop and train all-atom structure prediction models for antibody–antigen and protein–nucleic acid complexes.
• Diagnose failure modes and design architecture, training, and data interventions.
• Curate large-scale training data from structural databases and internal datasets.
• Integrate structure models with generative models for design and validation.
• Present results to colleagues in regular update meetings.

Skills

Biomolecular structure modeling

Deep learning (PyTorch/JAX)

Multi-GPU training

Education

PhD in computer science / computational biology / biophysics

Tools

PyTorch

JAX

Job description

Profluent Bio seeks an ML Scientist to drive biomolecular structure prediction research. You will build all-atom co-folding models focused on antibody–antigen interfaces and protein–nucleic acid assemblies, integrating with Profluent’s generative and experimental platforms.

The role requires a PhD or equivalent experience, hands-on model building, and publications or open-source contributions. Join an early ML team shaping AI-driven biology.