Scientist II / Senior ML Scientist, Data-Efficient Learning for Drug Discovery

About the Role

We are representing a confidential biotechnology client seeking a Machine Learning Scientist specializing in Data-Efficient Learning. This is a London-based opportunity to build cutting-edge ML models and learning strategies for settings where data is scarce, expensive, and intentionally generated—training useful models from datasets ranging from tens to low thousands of examples in focused areas of chemical space.

This applied scientific ML role operates at the frontier of research. You'll develop and deploy approaches across active learning, meta-learning, fine-tuning, uncertainty estimation, experimental design, and multimodal modeling to build closed-loop systems that learn efficiently from targeted data acquisition. Your work will directly connect model training with scientific decision-making in molecular optimization and discovery workflows.

Responsibilities

• Build ML models that perform well in low-data regimes for molecular optimization and discovery applications
• Design data acquisition strategies to identify which compounds, assays, simulations, or experiments should be prioritized next to maximize learning
• Develop active learning, meta-learning, transfer learning, and uncertainty-aware modeling approaches for focused chemical spaces
• Train models on low-quantity, high-quality datasets generated by experimental, computational, and automated discovery systems
• Build multimodal models integrating diverse data sources: screening data, simulation outputs, assay results, protein structural information, chemical features, and experimental metadata
• Partner with experimental, computational, and discovery teams to ensure data acquisition plans are scientifically meaningful and operationally feasible
• Evaluate models through learning curves, prospective validation, uncertainty calibration, and decision-focused metrics
• Develop closed-loop learning workflows that continuously update models as new data arrives
• Translate model predictions and uncertainty into practical recommendations for compound selection, assay selection, and experiment design
• Work with platform teams to expose model-driven recommendations as tools for scientists and automated systems

What We're Looking For

Required qualifications:

• PhD or equivalent experience in machine learning, computational chemistry, computational biology, statistics, computer science, bioengineering, or 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, or uncertainty estimation methods
• Experience building ML models for scientific, molecular, biological, chemical, or other high-dimensional experimental datasets
• Experience with multimodal learning or methods combining 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 discovery teams

Preferred qualifications:

• Experience in molecular optimization, screening, or design-make-test-learn workflows
• Understanding of pharmacology, biochemistry, or mechanisms of molecular activity
• Experience with high-throughput screening, medicinal chemistry, assay data, simulation-derived features, or protein structural features
• Experience with closed-loop experimentation, autonomous labs, or agent-driven scientific workflows
• Experience with generative molecular design, candidate prioritization, or batch selection workflows
• Familiarity with causal inference, optimal experimental design, decision theory, or Bayesian methods
• Comfort working with frontier ML techniques where standard approaches are insufficient

How We Work With You

Candidates apply directly through Get A Job.ai. Our talent team will screen applications and conduct initial interviews to understand your background and technical expertise. Qualified candidates will be submitted to our client for their review and interview process. Please do not attempt to contact the client directly—all communication should flow through Get A Job.ai to ensure a coordinated and professional process.

Pay

This is a full-time position based in London. The client offers competitive compensation including base salary, bonus potential, and equity. International salaries are set according to local market standards. Comprehensive benefits tailored to the UK region are provided.

Specific compensation details will be discussed during the interview process and will reflect your background, expertise, and expected impact.

Equal Employment Opportunity

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