Machine Learning and AI Specialist-Associate Principal Scientist Full-time

Machine Learning And Ai Specialist

Join AstraZeneca's Oncology R&D in our Beijing R&D Center as a Machine Learning and AI Specialist and help shape the future of drug discovery. At AstraZeneca, we are building a variety of AI powered tools to accelerate the drug discovery process. In this role you will be responsible for implementing AI architectures for prediction of properties and 3D structures of small and large molecules complexes. Together with your colleagues you will create enhanced data sets for retraining and focusing these methods for specific use cases. You will benchmark different methods and build optimal pipelines for delivering accurate and impactful predictions. You will operate in a collaborative environment with other specialists in China and globally within R&D, and you'll be encouraged to publish and present your work at leading conferences.

Expectations of a Successful Candidate

You will:

• Design, build and scale machine learning models (e.g., transformers, diffusion models, graph-based generative models) for molecular and biological property and structure prediction, leveraging large-scale molecular simulation, structural, affinity and potency data.
• Build scalable workflows: Create and maintain distributed computing pipelines on cloud/cluster environments (e.g. PyTorch, TensorFlow, DeepSpeed, Horovod, MPI, Nvidia Apex/AMP) and work with containerisation (e.g., Docker, Kubernetes) and orchestration.
• Own protein co-folding and prediction workflows: Lead the implementation, benchmarking and optimisation of protein-ligand and protein-protein complex prediction. Define metrics, analyse results and drive decisions on algorithmic direction.
• Champion innovation and best practice: Evaluate emerging computational methodologies and AI technologies; relevant to the role. Benchmark and optimise these methods to define best practise.
• Communicate and influence: Present complex results clearly to multidisciplinary audiences, guide experimental plans, and contribute to project strategy.
• Publish externally: In high-quality journals and present at national and international conferences.

Required Skills and Qualifications

• Education: PhD (or equivalent experience) in Computational Chemistry, Cheminformatics, Bioinformatics, Computer Science or a closely related discipline.
• Core expertise: Strong track record with machine learning and AI methods. Experience of a range of machine/deep learning algorithms and architectures (e.g. graph neural networks, transformers).
• AI application: Demonstrated interest and significant practical experience building and applying predictive or generative AI/ML methods in a chemical structure or protein structure context.
• Programming and workflows: Expert level proficiency with Python (and/or R, C++, Java), libraries for ML (e.g. scikit-learn, PyTorch, DeepChem, Tensorflow), modern optimisation techniques and experience with pipelining tools.
• Ways of working: Excellent communication, presentation, teamwork, influencing, and time management skills.

Desirable Skills and Qualifications

• Computational chemistry methods breadth: Knowledge and understanding of protein structure and dynamics modelling, and structure/ligand-based design.
• Experience: working in a drug discovery environment (industry or academia) is an advantage
• Experience with Protein Co-folding and Structure Prediction: Use of published tools to predict structure, including protein-ligand complexes; retraining and fine-tuning of models onto specific prediction tasks
• Large scale computing: experience with large-scale cloud computing, GPU acceleration and parallelisation
• Hands-on experience with databases, data warehousing and retrieval systems (e.g., SQL, NoSQL, graph databases, data lakes, large-scale data integration from chemical, assay and literature sources).
• Proven knowledge of deep-learning frameworks beyond the basics: e.g., Hugging Face Transformers, DGL – deep graph libraries, PyG (PyTorch Geometric), JAX/Flax, DeepSpeed, Megatron-LM.
• Familiarity with workflow orchestration and MLOps tooling: Airflow, Prefect, Dagster, Kubeflow, MLflow, DVC, clear understanding of CI/CD for ML, model monitoring, data drift detection.
• Publications: Peer-reviewed publications in computer science, computational chemistry, cheminformatics, or AI for drug discovery.

About AstraZeneca

AstraZeneca is a global, science-led biopharmaceutical company committed to transforming patients' lives through innovative medicines. In Oncology R&D, we combine deep biological insight with state-of-the-art AI to accelerate molecular design and decision-making. Our teams operate in an open, collaborative environment across Beijing (China), Cambridge (UK) and Boston (USA), sharing best practice and pushing the boundaries of computational chemistry and machine learning. By joining us as a Associate Principal Scientist, you will contribute to a vibrant community of scientists pioneering computer-aided drug design – and have the platform to publish, present, and shape the next wave of innovation.