Fundamental AI Research Scientist - Toronto, Ontario

• You will work efficiently in a team to work on fundamental AI research problems, customise solutions to various applications 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 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, genetic engineering, synthetic 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.

• 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 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 AI/ML architectures and training algorithms, (iii) analyse 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 visualisation.

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

• 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 with hands-on practical experience and expert at least one 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.

• 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, and molecular, cellular and tissue biology.

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

AstraZeneca