Fundamental AI Research Scientist – Toronto, Ontario Full-time

Artificial Intelligence Scientist

At AstraZeneca, we pride ourselves on crafting a collaborative culture that champions knowledge-sharing, ambitious thinking and innovation – ultimately providing employees with the opportunity to work across teams, functions and even the globe.

Recognizing the importance of individualized flexibility, our ways of working allow employees to balance personal and work commitments while ensuring we continue to create a strong culture of collaboration and teamwork by engaging face-to-face in our offices 3 days a week. Our head office and BlueSky Hub in downtown Toronto are purposely designed with collaboration in mind, providing space where teams can come together to strategize, brainstorm and connect on key projects.

Our dedication to sustainability is also central to our culture and part of what makes AstraZeneca a great place to work. We know the health of people, the planet and our business are interconnected which is why we're taking ambitious action to tackle some of the biggest challenges of our time, from climate change to access to healthcare and disease prevention.

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 optimization and search? What about advancing our understanding of biology, streamlining research and development processes, and bringing to bear a variety of data modalities? Do you thrive working in an encouraging, inclusive environment where creativity, collaboration across fields, and lifelong learning towards innovative breakthroughs are encouraged? 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. You will apply 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 optimization 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 analyzing 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:

• Candidates are required to have a PhD or comparable experience in machine learning, statistics, computer science, mathematics, physics, or a related technical field. They should possess fundamental research experience in artificial intelligence and machine learning. Alternatively, an MSc or equivalent experience combined with several years of relevant research and development work in artificial intelligence and machine learning for life sciences or similar applied experience is acceptable.
• 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. In addition, experience in applying rigorous scientific methodology to (i) identify and create ML techniques and the required data to train models, (ii) develop machine learning model architectures and training algorithms, (iii) analyze and fine tune experimental results to inform future experimental directions, and (iv) implement and scale training and inference engineering frameworks and (v) validate hypotheses.
• Theoretical understanding, combined with a strong quantitative knowledge of algebra, algorithms, probability, calculus, and statistics, hands-on experimentation, analysis, and AI/ML techniques visualization.
• 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. 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 – examples include but are not limited to – multi-agent systems, logic, causal inference, Bayesian optimization, experimental design, deep learning, reinforcement learning, non-convex optimization, 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 optimization, artificial neural networks, multi-scale modeling, transfer learning, mathematical optimization and simulation, planning and control modeling, 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 modeling, 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 sciences approaches.
• Fluent in Python, R, and/or Julia; 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). Examples include but are not limited to 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.