Senior ML Scientist, Biological Systems
San Francisco, CA$268k - $358kfull-timePosted 1mo ago
About the role
Senior ML Scientist focusing on domain models for perturbation biology, genetics, and multimodal experimental data to connect biological mechanisms with experimental interventions and therapeutic opportunities. Builds interpretable models, handles uncertainty, and collaborates with experimental scientists to guide data generation and model validation.
Responsibilities
- Build domain models for perturbation, genetic, and multimodal experimental data, translating biological questions into rigorous ML problem formulations with interpretable structure where possible.
- Make uncertainty a deliverable: calibrated posteriors, honest error bars, and outputs that convey how much a prediction can settle in unmeasured conditions.
- Pre-register and beat baselines; if the full model does not beat simple baselines, ship the baseline and report.
- Partner with experimental scientists to guide data generation and model validation, shaping what gets measured and in what contexts, and develop experiment-selection methods to reduce uncertainty where it matters.
- Design benchmarks linking model performance to biological and therapeutic outcomes; apply model outputs to prioritize targets and mechanisms; contribute technical direction to high-impact modeling programs.
- Support the integration of domain models into agentic workflows; deploy tools and collaborate on environments to train reasoning models that drive agents.
Qualifications
- PhD in machine learning, statistics, computational biology, computer science, bioengineering, physics, or related quantitative field, with strong publication record or equivalent impact.
- Experience developing models for high-dimensional biological data and connecting ML methods to biological mechanisms and experimental design.
- Generalization under structured sparsity: track record of predicting into unseen conditions, handling sparse/unbalanced designs, and transferring to new contexts.
- Uncertainty and evaluation rigor: experience with calibration, proper scoring rules, and evaluation design in decision-making contexts.
- Strong programming skills, reliable ML research workflows, and ability to communicate across ML, biology, and experimental teams; track record of leading ambiguous research problems.
Skills
Required:Domain modeling for biological dataUncertainty quantificationExperiment design and data generation collaboration
Benefits
- Full-time comprehensive benefits program (medical, dental, vision) for U.S. employees; life and disability insurance; flexible time off; company holidays; parental leave; educational assistance; commuter benefits; lunch program.
Last updated Sep 16, 2026. Interested? Apply on the company site.
About Lila Sciences
Pioneering Scientific Superintelligence
Pioneering Scientific Superintelligence to solve humankind’s greatest challenges.
Scientific SuperintelligenceAIAutonomous LabsLife SciencesChemical SciencesMaterials Sciences
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