Scientist II / Senior ML Scientist, Data-Efficient Learning for Drug Discovery
Core
Build data-efficient ML models and learning strategies for drug discovery in low-data regimes, guiding targeted data acquisition for chemical space exploration.
Role type
Senior IC machine learning scientist (data-efficient learning)
Builds
Production ML models, data acquisition strategies, and closed-loop learning workflows for molecular optimization and drug discovery.
Domain
Biopharma / Drug Discovery / Machine Learning
Deliverable
production ML models
Required skills
Active learning, meta-learning, fine-tuning, transfer learning, uncertainty estimation, experimental design, multimodal modeling, low-data regime training, PyTorch, JAX, scikit-learn
Preferred skills
Drug discovery experience, DEL/high-throughput screening, closed-loop experimentation, generative molecular design, causal inference, Bayesian methods
Technologies
PyTorch, JAX, scikit-learn
Responsibilities
Train models on low-quantity, high-quality datasets; design data acquisition strategies; develop multimodal models integrating heterogeneous data; partner with experimental and computational teams; evaluate models via learning curves and prospective validation; translate predictions into practical recommendations for compound selection.
Seniority
Senior, hands-on IC
