Senior AI Research Scientist

Are you passionate about creating artificial intelligence and machine learning systems for real-world science applications? Does contributing to preventing, modifying, and even curing some of the world’s most complex diseases inspire you? Would you like to work on developing an iterative drug discovery and development process while drawing on methods across various fields, from active learning to optimisation and search? What about advancing our understanding of biology, streamlining research and development processes, and bringing to bear a variety of data modalities? If yes, this opportunity may be for you.

Join our interdisciplinary Centre for Artificial Intelligence team working on the frontier of AI research for digital biology. Your work will support the next generation of medicines and vaccines at the intersection of AI, biology, and engineering. Your work will contribute to transforming the drug discovery and development value chain as we know it, uncovering novel biological insights, automating processes, streamlining decisions, and improving the overall pipeline across all therapeutic areas at AstraZeneca.

Key Responsibilities

- You will work efficiently in a team to deliver projects optimally by researching, developing, and using novel AI theories, methodologies, and algorithms, and applying engineering guidelines and standard processes for biology, chemistry, and clinical applications.

- You will be part of multifunctional teams 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 to support the discovery, design, and optimisation of medicines with improved biological activity.

- You will contribute to addressing challenges and opportunities in the drug discovery and development value chain processes and provide innovative solutions in fields such as deep learning, representation learning, reinforcement learning, meta‑learning, active learning approaches applied to de‑novo molecule design, protein engineering, in‑silico discovery, structural biology, computational biology, translational sciences, biomarker discovery, clinical research, clinical trials and many other areas.

- You will develop machine learning models designed explicitly for analysing heterogeneous biological data while collaborating with biology researchers to run algorithmically designed wet‑lab experiments to inform future experimental directions.

- You will remain at the forefront of AI/ML research by participating in journal clubs, seminars, mentoring, and personal development initiatives and contributing to publications and academic and industry collaborations.

Essential Education, Experience & Skills

- PhD or comparable experience in machine learning, statistics, computer science, mathematics, physics, or a related technical field, with fundamental research experience in artificial intelligence and machine learning; alternatively, MSc or equivalent experience combined with several years of relevant research and development work in AI/ML for life sciences or similar applied experience.

- Fundamental AI research and development experience with well‑rounded hands‑on ability to implement AI/ML techniques based on publications or developed entirely in‑house, and experience applying rigorous scientific methodology to identify and create ML techniques, develop machine learning model architectures and training algorithms, analyse and fine‑tune experimental results, implement and scale training and inference engineering frameworks, and validate hypotheses.

- Theoretical understanding combined with strong quantitative knowledge of algebra, algorithms, probability, calculus and statistics, hands‑on experimentation, analysis, and AI/ML technique visualisation.

- Algorithmic development and programming experience in Python or other programming languages and machine learning toolkits, especially deep learning (e.g., PyTorch, TensorFlow, etc.).

- Experience in practical aspects of AI/ML foundations and model design, such as improving experimentation and analysis of model efficiency, quantisation, conditional computation, reducing bias, or achieving explainability in complex models.

- Ability to communicate and collaborate effectively with diverse individuals and functions, and to report and present research findings clearly and efficiently to scientists, engineers and domain experts from different fields.

- Fundamental research with hands‑on practical experience and theoretical knowledge of at least one of the following research areas: multi‑agent systems, logic, causal inference, Bayesian optimisation, experimental design, deep learning, reinforcement learning, non‑convex optimisation, Bayesian non‑parametric methods, natural language processing, approximate inference, control theory, meta‑learning, category theory, statistical mechanics, information theory, knowledge representation, unsupervised, supervised, semi‑supervised learning, computational complexity, search and optimisation, artificial neural networks, multi‑scale modelling, transfer learning, mathematical optimisation and simulation, planning and control modelling, time series foundation models, federated learning, game theory, statistical inference, pattern recognition, large language models, probability theory, probabilistic programming, Bayesian statistics, applied mathematics, multimodality, computational linguistics, representation learning, foundations of generative modelling, computational geometry and geometric methods, multi‑modal deep learning, information retrieval and/or related areas.

Desirable Experience & Skills

- Experience designing new AI/ML approaches to deriving insights from proprietary and external datasets to generate testable hypotheses using algorithmic, mathematical, computational, and statistical methods combined with theoretical, empirical or experimental research science approaches.

- Fluent in Python, R, and/or Julia, and other programming languages including scientific packages and libraries (e.g., PyTorch, TensorFlow, Pandas, NumPy, Matplotlib).

- Research experience demonstrated by journal and conference publications in prestigious venues (with at least one publication as a leading author) such as NeurIPS, ICML, ICLR and JMLR.

- Practical ability to work on cloud computing environments like AWS, GCP, and Azure.

- Domain knowledge of tools, techniques, methods, software and approaches in one or more areas such as protein engineering, microbiology, structural biology, molecular design, biochemistry, genomics, genetics, bioinformatics, molecular, cellular, tissue biology.

- Evidence of open‑source projects, patents, personal portfolios, products, peer‑reviewed publications or similar track records.

AstraZeneca is an equal opportunity employer committed to diversity and inclusion and providing a workplace that is free from discrimination. AstraZeneca is committed to accommodating persons with disabilities. Such accommodation is available on request in respect of all aspects of the recruitment, assessment and selection process and may be requested by emailing AZHumanResources@astrazeneca.com.

AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry‑leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non‑discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.

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