Senior ML Scientist, Biological Systems
Core
Build domain models for perturbation, genetic, and multimodal experimental data to connect biological mechanisms with therapeutic opportunities, focusing on interpretable structures and uncertainty quantification.
Role type
Senior IC machine learning scientist (biological systems)
Builds
Domain models for high-dimensional biological data, experiment-selection methods, and agentic workflows for scientific decision-making
Domain
Life sciences / computational biology / AI for drug discovery
Deliverable
production ML models
Required skills
High-dimensional biological data modeling, Generalization under structured sparsity, Uncertainty quantification and calibration, Bayesian hierarchical modeling, Mechanistic and probabilistic modeling, Active learning and optimal experimental design, Model deployment in scientific workflows, Research problem formulation and execution
Preferred skills
Neural differential equations, Simulation-based inference, Lab-in-the-loop systems, Pharmacokinetic/pharmacodynamic modeling
Technologies
Bayesian inference frameworks, ODE solvers, Active learning libraries, Model serving infrastructure
Responsibilities
Translate biological questions into rigorous ML problem formulations, Partner with experimental scientists to guide data generation and model validation, Design benchmarks connecting model performance to biological consequence, Support integration of domain models into agentic workflows, Lead ambiguous research problems from formulation through execution, Communicate findings to technical and cross-functional audiences
Seniority
Senior, hands-on IC with research leadership