Senior ML Scientist, Biological Systems
$268k - $358k • San Francisco, CA USA • Full-time
Posted 1mo ago
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
Lila is revolutionizing medicine by integrating automated large-scale data generation with scientific superintelligence. We are building a system where AI, automation, and experimental biology work together to tackle complex medical challenges. The Life Science AI team focuses on developing machine learning systems for analyzing biological data, combining advanced ML with cutting-edge biology. This role is crucial for building domain models that understand perturbation biology, genetics, and high-dimensional experimental data, linking biological mechanisms, experimental interventions, and therapeutic opportunities. The work involves not just learning from data but also guiding data generation and ensuring model outputs are interpretable and actionable for scientists and downstream systems.
Responsibilities
- Build domain models for perturbation, genetic, and multimodal experimental data, translating biological questions into ML problem formulations.
- Develop models with interpretable structure, grounding representations in biological processes and ensuring parameters are understandable to biologists.
- Quantify uncertainty in model predictions through calibrated posteriors and honest error bars.
- Establish and beat baseline performance metrics before deploying full models.
- Collaborate with experimental scientists to guide data generation and model validation, shaping experimental design and building methods for selecting future measurements.
- Design benchmarks that link model performance to biological and therapeutic outcomes.
- Apply model outputs to prioritize targets and mechanisms, providing technical direction for high-impact modeling programs.
- Support the integration of domain models into agentic workflows by deploying tools and engineering agent harnesses.
- Create environments for training reasoning models that drive AI agents.
Requirements
- PhD in machine learning, statistics, computational biology, computer science, bioengineering, physics, or a related quantitative field, with a strong publication record or equivalent industry impact.
- Experience developing models for high-dimensional biological data and connecting ML methods to biological mechanisms and experimental design.
- Proven ability to build models that generalize under structured sparsity, predicting into unobserved conditions.
- Proficiency in uncertainty quantification, calibration, proper scoring rules, and evaluation design.
- Strong programming skills and 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 clearly to technical and cross-functional audiences.
- Experience supporting external scientific visibility through publications and presentations.
Benefits
- Competitive base compensation with bonus potential
- Generous early-stage equity
- Comprehensive medical, dental, and vision coverage (U.S.)
- Employer-paid life and disability insurance (U.S.)
- Flexible time off with generous company-wide holidays (U.S.)
- Paid parental leave (U.S.)
- Educational assistance program (U.S.)
- Commuter benefits, including bike share memberships (U.S.)
- Company-subsidized lunch program (U.S.)
- Comprehensive benefits program tailored to region (International)