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ML Scientist I / II, Foundation Models for Life Sciences

Lila Sciences
Location
San Francisco, CA USA
Posted
2 months ago
Department
AI
What they actually want (must-haves)
  • PhD in Computer Science, Machine Learning, Computational Biology, Biophysics, or a 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
  • Ability to formulate and execute research independently
  • 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
Nice to have
  • Experience training or extending co-folding, structure prediction, protein–protein, or diffusion deep learning models
  • Experience with AlphaFold or AlphaFold-derived methods
  • Antibody, biologics, or protein design experience
  • Familiarity with distributed training infrastructure and large-scale scientific data pipelines
  • Contributions to open-source ML tools, frameworks, or benchmark datasets for scientific applications
What the job really is

As a Scientist I or II at Lila, you will focus on structure prediction and co-folding, particularly in protein–protein interactions to aid in antibody and biologics design. Your role involves training and evaluating models, collaborating with experimental scientists, and contributing to the development of Lila's AI platform for scientific discovery. You will also engage in the end-to-end machine learning process, shaping data strategies and ensuring model performance through feedback loops with experimental results.

Benefits
  • Competitive base compensation with bonus potential and generous early-stage equity
  • Comprehensive benefits program including medical, dental, and vision coverage
  • Employer-paid life and disability insurance
  • Flexible time off with generous company-wide holidays
  • Paid parental leave
  • Educational assistance program
Things to weigh
  • No specific mention of the exact technologies or methodologies used in the team's current projects beyond general terms
  • The role requires close collaboration with experimental scientists, which may require strong communication skills and adaptability
  • The position is described as an individual contributor role, which may not suit those looking for more collaborative or team-oriented environments
Job score2.8/5
Benefits4/5
Freshness1/5
Career value5/5
Role clarity4/5
Pay transparency0/5

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