Associate Principal AI Research Scientist (Fundamental AI Research for Digital Biology)

AstraZeneca • Boston, MA

1d • Hybrid

About The Position

Accountabilities: •You will work efficiently in a team to lead and deliver projects optimally, researching, developing and using the novel AI theories, methodologies, and algorithms, with engineering best practices and standard processes for various biology, chemistry and clinical applications. •You will be part and also lead 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 to support the discovery, design, and optimisation of medicines with improved biological activity. •You will lead and 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, genetic engineering, synthetic biology, computational biology, translational sciences, biomarker discovery, clinical research, clinical trials and many other areas. •You will lead and 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 Skills/Experience: •A PhD in machine learning, statistics, computer science, mathematics, physics, or a related technical discipline with relevant fundamental research experience in artificial intelligence and machine learning or equivalent practical experience. •Fundamental AI research experience in conjunction with foundational knowledge and a proven 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, combined with a strong quantitative knowledge of algebra, algorithms, probability, calculus, and statistics, as well as extensive hands-on experimentation analysis, and AI/ML techniques visualisation. •Well-rounded 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 sciences 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 applying rigorous scientific methodology to (i) identify and create novel ML techniques and the required data to train models, (ii) develop machine learning model' architectures and training algorithms, (iii) analyse and tune experimental results to inform future experimental directions, and (iv) implement and scale training and inference engineering frameworks and (v) validate hypotheses. •Distinctive experience in exploiting the simplest tricks to the latest research methods to advance AI/ML capabilities while implementing them in an elegant, stable, and scalable way. •Thorough algorithmic development and programming experience in Python or other programming languages and standard machine learning toolkits, especially deep learning (e.g., Pytorch, TensorFlow, etc.). •Robust ability to communicate and collaborate effectively with diverse individuals and functions, reporting and presenting research findings and developments clearly and efficiently to other scientists, engineers and domain experts from different disciplines. •Fundamental research, extensive research and expert understanding combined with hands-on practical experience and theoretical knowledge of at least two or more of the following research areas - examples include but are not limited to - multi-agent systems, logic, causal inference, Bayesian optimisation, experimental design, deep learning, reinforcement learning, non-convex optimisation, Bayesian non-parametric, 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 Skills/Experience: •Fluent in Python, R, and/or Julia other programming languages, including scientific packages and libraries (e.g. PyTorch, TensorFlow, Pandas, NumPy, Matplotlib). •Experience in machine learning research and developing fundamental algorithms and frameworks that can be applied to various machine learning problems, particularly in biology, chemistry and clinical applications and a demonstrated track record for solving biological issues relevant to drug discovery and development. •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. •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 and tissue biology. •Evidence of open-source projects, patents, personal portfolios, products, peer-reviewed publications, or similar track records. Why AstraZeneca? When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person work 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. 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! Date Posted 09-mar-2026 Closing Date 22-mar-2026 Our mission is to build an inclusive environment where equal employment opportunities are available to all applicants and employees. In furtherance of that mission, we welcome and consider applications from all qualified candidates, regardless of their protected characteristics. If you have a disability or special need that requires accommodation, please complete the corresponding section in the application form. AstraZeneca is a global, science-led, patient-focused biopharmaceutical company. We focus on discovering, developing and commercialising prescription medicines for some of the world’s most serious diseases. But we are more than one of the world’s leading pharmaceutical companies. At AstraZeneca, we’re dedicated to being a Great Place to Work. Where you are empowered to push the boundaries of science, challenge convention and unleash your entrepreneurial spirit. To embrace differences and take bold actions to drive the change needed to meet global healthcare and sustainability challenges. There is no better place to make a difference in medicine, patients, and society. An inclusive culture where you will connect different thinking to generate new and valuable opportunities. Where you will find a commitment to lifelong learning, growth and development for all. Our Inclusion & Diversity (I&D) mission is to create an inclusive and equitable environment where people belong, using the power of our diversity to push the boundaries of science to deliver life-changing medicines to patients. Inclusion and diversity are fundamental to the success of our company, because innovation requires breakthrough ideas that only come from a diverse workforce empowered to challenge conventional thinking. We’re curious about science and the advancement of knowledge. We find creative ways to approach new challenges. We’re driven to make the right choices and be accountable for our actions. As an organisation centred around what makes us human, we put a big focus on people. Across our business, we want colleagues to wake up excited about their day at the office, in the field, or in the lab. Along with our purpose to bring life-changing medicines to people across the globe, we have a promise to you: to help you realise the full breadth of your potential. Here, you’ll do work that has the potential to change your life and improve countless others. And, together with your team, you’ll shape a culture that unites and inspires us every day. This is your life at AstraZeneca.

Requirements

• A PhD in machine learning, statistics, computer science, mathematics, physics, or a related technical discipline with relevant fundamental research experience in artificial intelligence and machine learning or equivalent practical experience.
• Fundamental AI research experience in conjunction with foundational knowledge and a proven 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, combined with a strong quantitative knowledge of algebra, algorithms, probability, calculus, and statistics, as well as extensive hands-on experimentation analysis, and AI/ML techniques visualisation.
• Well-rounded 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 sciences 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 applying rigorous scientific methodology to (i) identify and create novel ML techniques and the required data to train models, (ii) develop machine learning model' architectures and training algorithms, (iii) analyse and tune experimental results to inform future experimental directions, and (iv) implement and scale training and inference engineering frameworks and (v) validate hypotheses.
• Distinctive experience in exploiting the simplest tricks to the latest research methods to advance AI/ML capabilities while implementing them in an elegant, stable, and scalable way.
• Thorough algorithmic development and programming experience in Python or other programming languages and standard machine learning toolkits, especially deep learning (e.g., Pytorch, TensorFlow, etc.).
• Robust ability to communicate and collaborate effectively with diverse individuals and functions, reporting and presenting research findings and developments clearly and efficiently to other scientists, engineers and domain experts from different disciplines.
• Fundamental research, extensive research and expert understanding combined with hands-on practical experience and theoretical knowledge of at least two or more of the following research areas - examples include but are not limited to - multi-agent systems, logic, causal inference, Bayesian optimisation, experimental design, deep learning, reinforcement learning, non-convex optimisation, Bayesian non-parametric, 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.

Nice To Haves

• Fluent in Python, R, and/or Julia other programming languages, including scientific packages and libraries (e.g. PyTorch, TensorFlow, Pandas, NumPy, Matplotlib).
• Experience in machine learning research and developing fundamental algorithms and frameworks that can be applied to various machine learning problems, particularly in biology, chemistry and clinical applications and a demonstrated track record for solving biological issues relevant to drug discovery and development.
• 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.
• 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 and tissue biology.
• Evidence of open-source projects, patents, personal portfolios, products, peer-reviewed publications, or similar track records.

Responsibilities

• Work efficiently in a team to lead and deliver projects optimally, researching, developing and using the novel AI theories, methodologies, and algorithms, with engineering best practices and standard processes for various biology, chemistry and clinical applications.
• Be part and also lead 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 to support the discovery, design, and optimisation of medicines with improved biological activity.
• Lead and 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, genetic engineering, synthetic biology, computational biology, translational sciences, biomarker discovery, clinical research, clinical trials and many other areas.
• Lead and 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.
• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree