Are you passionate about creating artificial intelligence and machine learning models, algorithms, and tools 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 designing and 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 leveraging a variety of data modalities? Do you thrive working in a supportive, inclusive environment where creativity, collaboration across disciplines and lifelong learning towards innovative breakthroughs are encouraged? If yes, this opportunity may be for you.

Join our interdisciplinary Centre for Artificial Intelligence team in partnership with Biologics Engineering, which is responsible for discovering, designing, and… optimising the next-generation biological drug candidates across all key therapeutic areas at AstraZeneca. Your work will contribute to uncovering biological insights, automating processes, streamlining decisions, and improving the overall pipeline throughout the biologics engineering value chain.

 We have a variety of positions at different levels.


•You will work efficiently in a team to deliver projects optimally using the latest AI/ML methods, approaches, and techniques, with engineering best practices and standard processes.

•You will be part of multifunctional teams, particularly with our partners from Biologics Engineering, to develop machine learning methods and tools for discovering, designing, and optimising large molecules such as antibodies with improved biological activity.

•You will analyse 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 protein design, protein engineering, in-silico discovery, structural biology, computational biology, and many other areas.

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

Essential Skills/Experience:

•A PhD in computer science, statistics, mathematics, physics, biology or related field OR MSc and proven experience developing artificial intelligence and machine learning models.

•Experience with deep learning methods and their applications, particularly to problems in biology and chemistry.

•Knowledge of computational biology and demonstrated experience in incorporating it into machine learning models.

•Understanding of the AI/ML lifecycle, including data handling, feature engineering, model training, and optimisation.

•Ability to exploit the simplest tricks to the latest research methods to advance AI/ML capabilities while implementing them in an elegant, stable, and scalable way.

Desirable Skills/Experience:

•Research experience demonstrated by journal and conference publications in prestigious venues (with at least one publication as a leading author). Examples include but are not limited to NeurIPS, ICML, ICLR and JMLR.

•A track record of successfully collaborating with AI engineering teams to deliver complex machine learning models and production-ready data and analytics products.

•Demonstrated experience incorporating them into artificial intelligence approaches and machine learning models.

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

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

•Experience in end-to-end research, development and implementation of artificial intelligence and machine learning solutions that apply existing algorithmic, mathematical, computational, and statistical approaches and might advance the state-of-the-art, with a practical, in-depth understanding of suitable approaches for a particular problem.

•In-depth knowledge of one or more of the following areas such as causal inference, machine learning, deep learning, control theory, natural language processing, multi-scale modelling, reinforcement learning, mathematical optimisation and simulation, signal processing, game theory, statistical inference, operations research, pattern recognition, recommendation systems, probabilistic programming and/or related areas.

•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 and tissue biology.

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

When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That’s why we work, on average, a minimum of three days per week from the office. But that doesn’t mean we’re not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world.

 Why AstraZeneca?

Join the team, unlocking the power of what science can do. We are working towards treating, preventing, modifying and even curing some of the world’s most complex diseases. Here, we have the potential to grow our pipeline and positively impact the lives of billions of patients around the world. We are committed to making a difference. We have built our business around our passion for science. Now, we are fusing data and technology with the latest scientific innovations to achieve the next wave of breakthroughs.

Ready to make a difference? Apply now and join us in our mission to push the boundaries of science and deliver life-changing medicines!

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


10001253 – Associate Principal AI Scientist


Language requirements:

Specific requirements:

Educational level:

Level of experience (years):

Senior (5+ years of experience)

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