Senior Data-Efficient ML Scientist for Drug Discovery

Lilasciences; Greater London; On-site; GBP 170,000 - 267,000; Full time

Benefits offered by this job

Generous equity

Bonus potential

Educational assistance

Job summary

Lila Sciences is seeking a Machine Learning Scientist to develop data-efficient ML methods for drug discovery, focusing on low-quantity datasets and targeted chemical spaces. This role combines active learning, meta-learning, uncertainty estimation, and experimental design to guide data acquisition and model-driven decision making.

You will build multimodal models integrating DEL data, simulations, assays, and structural information, and collaborate with experimental and computational teams to

Qualifications

• PhD or equivalent experience in machine learning, computational chemistry, computational biology, statistics, computer science, bioengineering, or a related field.
• Strong experience training ML models in low-data regimes.
• Experience with active learning, Bayesian optimization, experimental design, meta-learning, fine-tuning, transfer learning, uncertainty estimation, or related data-efficient learning methods.
• Experience building ML models for scientific, molecular, biological, chemical, pharmacological, biochemical, or other high-dimensional experimental datasets.
• Experience with multimodal learning or methods that combine heterogeneous data sources.
• Ability to reason about data acquisition strategy, not only model fitting.
• Strong scientific judgment and ability to connect model behavior to experimental decisions.
• Practical experience with PyTorch, JAX, scikit-learn, or equivalent ML tools.
• Ability to collaborate across ML, data, computational science, experimental, and drug discovery teams.

Responsibilities

• Build ML models that perform well in low-data regimes for drug discovery and molecular optimization.
• Design data acquisition strategies that identify which compounds, assays, DEL selections, simulations, structural predictions, or experiments should be run next to maximize learning.
• Develop active learning, meta-learning, fine-tuning, transfer learning, and uncertainty-aware modeling approaches for focused chemical spaces.
• Train models on low-quantity, high-quality datasets generated by Lila's experimental, computational, and agentic discovery systems.
• Build multimodal models that can integrate DEL data, simulation outputs, assay data, protein and structural information, chemical features, literature or text-derived signals, images, and experimental metadata.
• Partner with experimental, computational, and drug discovery teams to ensure data acquisition plans are scientifically meaningful and operationally feasible.
• Evaluate models through learning curves, prospective validation, retrospective benchmarks, uncertainty calibration, and decision-focused metrics.
• Develop closed-loop learning workflows that continuously update models as new data arrives from experiments, simulations, and automated systems.
• Translate model predictions and uncertainty into practical recommendations for compound selection, assay selection, batch design, or next experiments.
• Work with platform and agent teams to expose model-driven recommendations as tools for scientists and AI agents.

Skills

Active learning

Bayesian optimization

Experimental design

Meta-learning

Fine-tuning

Transfer learning

Uncertainty estimation

Multimodal learning

PyTorch

JAX

scikit-learn

Education

PhD or equivalent in ML or related field

Tools

PyTorch

JAX

scikit-learn

Job description

Lila Sciences is seeking a Machine Learning Scientist to develop data-efficient ML methods for drug discovery, focusing on low-quantity datasets and targeted chemical spaces. This role combines active learning, meta-learning, uncertainty estimation, and experimental design to guide data acquisition and model-driven decision making.

You will build multimodal models integrating DEL data, simulations, assays, and structural information, and collaborate with experimental and computational teams to