Associate Principal AI Research Scientist; Fundamental AI Research Digital Biology

Accountabilities

• Work as part of a high‑performing team to lead and deliver research projects
  , researching, developing and using novel AI theories, methodologies and algorithms with engineering best practices for a range of biology, chemistry and clinical applications.
• Lead and contribute to multifunctional projects to conceive, design, develop and conduct experiments to test hypotheses, validate new approaches and compare the effectiveness of different AI/ML systems, algorithms, methods and tools for new applications that support the discovery, design and optimisation of medicines with improved biological activity.
• Address fundamental AI research challenges and opportunities across the drug discovery and development value chain, providing innovative solutions in areas such as deep learning, representation learning, reinforcement learning, meta‑learning, active learning, search and optimisation, applied to domains including de novo molecule design, protein engineering, in‑silico discovery, structural biology, genetic engineering, synthetic biology, computational biology, translational sciences, biomarker discovery, clinical research and clinical trials.
• Design and develop machine learning models for heterogeneous biological data
  , collaborating with experimental scientists (e.g. in chemistry, discovery science and other experimental fields) to plan and interpret algorithmically designed wet‑lab experiments and inform future experimental directions.
• Translate complex scientific requirements into AI research problems and solution strategies
  , exploring different approaches and reasoning about trade‑offs to tackle diverse, complex challenges across multiple projects.
• Stay at the forefront of AI/ML research by participating in journal clubs, seminars, mentoring and personal development initiatives, and by contributing to publications and academic/industry collaborations.

• PhD in machine learning, statistics, computer science, mathematics, physics or a related technical discipline, with relevant fundamental research experience in AI/ML
  , or equivalent practical experience.
• Fundamental AI research experience with a strong track record in conceptualising, designing and creating entirely new models, methods, approaches, architectures and algorithms from scratch
  . This is essential, as off‑the‑shelf methods and state‑of‑the‑art AI/ML techniques often do not work on our scientific problems and datasets.
• Deep theoretical understanding and strong quantitative knowledge of algebra, algorithms, probability, calculus and statistics
  , combined with extensive hands‑on experience in experimentation, analysis and visualisation of AI/ML techniques.
• Well‑rounded experience designing new AI/ML approaches to derive insights from proprietary and external datasets and to generate testable hypotheses, using algorithmic, mathematical, computational and statistical methods combined with theoretical, empirical or experimental research approaches.
• Experience in theoretical, fundamental AI research and practical aspects of AI/ML foundations and model design, such as improving model efficiency, quantisation, conditional computation, reducing bias or achieving explainability in complex models.
• In‑depth understanding of rigorous scientific methodology to:

Identify and create novel ML techniques and the data required to train models;
Develop machine learning model architectures and training algorithms;
Analyse and tune experimental results to inform future experimental directions;
Implement and scale training and inference frameworks;
Validate hypotheses in a reproducible manner

• Distinctive experience in using anything from simple baseline tricks to cutting‑edge research methods to advance AI/ML capabilities, and in implementing them in an elegant, stable and scalable way.
• Strong algorithmic development and programming experience in Python or similar languages, and standard machine learning toolkits, especially deep learning frameworks such as PyTorch, Tensor Flow or similar.
• Robust ability to communicate and collaborate effectively with diverse stakeholders
  , clearly presenting research findings and developments…