Senior ML Scientist, Biological Systems

The role

The Senior ML Scientist will build domain models for perturbation, genetic, and multimodal experimental data. This person will translate biological questions into ML problem formulations and partner with experimental scientists to guide data generation.

What you will do

• Build domain models for perturbation, genetic, and multimodal experimental data
• Translate biological questions into rigorous ML problem formulations
• Build models with interpretable structure
• Make uncertainty a deliverable through calibrated posteriors and honest error bars
• Pre-register and beat baselines
• Partner with experimental scientists to guide data generation and model validation
• Build experiment-selection methods to reduce uncertainty
• Design benchmarks connecting model performance to biological and therapeutic consequence
• Support the integration of domain models into agentic workflows
• Support external scientific visibility through publications and presentations

Required qualifications

• PhD in machine learning, statistics, computational biology, computer science, bioengineering, physics, or a related quantitative field
• Strong publication record or equivalent industry impact
• Experience developing models for high-dimensional biological data
• Track record of building models that predict into conditions not directly observed
• Comfort with calibration, proper scoring rules, and evaluation design
• Strong programming skills
• Reliable ML research workflows
• Clear communication across ML, biology, and experimental teams
• Track record of leading ambiguous research problems from formulation through execution
• Ability to communicate findings to technical and cross-functional audiences

Nice to have

• Mechanistic and probabilistic modeling
• Bayesian hierarchical models
• Simulation-based or likelihood-free inference
• Amortized posterior inference
• State-space or ODE-based models
• Neural differential equations
• Mechanism-informed ML
• Pharmacokinetic/pharmacodynamic, systems-biology, or physical modeling
• Familiarity with active learning, Bayesian optimal experimental design, closed-loop experimentation, or lab-in-the-loop systems
• Experience deploying research models into scientific decision-making workflows

Benefits

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

About the company

Lila Sciences is building Scientific Superintelligence to solve humankind's greatest challenges. The company combines advanced AI models with proprietary AI Science Factory instruments to execute the scientific method autonomously.