ML Scientist, Foundation Models for Life Sciences

Highlights

• Train structure prediction models to drive antibody and biologics design.
• Close the loop between computation and wet-lab experimental decisions.
• Build foundation models across sequence, structure, and experiment data.

What they offer

• Bonus potential
• Generous early-stage equity
• Medical, dental, and vision coverage
• Employer-paid life and disability insurance
• Flexible time off with company holidays
• Paid parental leave
• Educational assistance program
• Commuter benefits
• Company subsidized lunch program

Ideal candidate

PhD-level researcher passionate about structure-aware AI for biology who thrives owning research end-to-end and collaborating with wet-lab scientists to turn predictions into real discoveries.

Requirements

• PhD in Computer Science, Machine Learning, Computational Biology, Biophysics, or related quantitative field (or Master's with equivalent research experience)
• Hands-on experience training deep learning models on molecular, protein, or structural data
• Strong foundation in generative model architectures and training, with careful experiments, ablations, and evaluations
• Ability to formulate and execute research independently, from problem definition through experimentation
• Familiarity with at least one life science domain (structural biology, protein engineering, molecular biology, genomics, or related)
• Experience collaborating with experimental scientists or working with biological/chemical data
• Proficiency in ML frameworks (PyTorch, JAX, or TensorFlow) and GPU-based training workflows