Senior Expert II (Biomedical AI Methods Expert

Role Summary

Senior Expert II (Biomedical AI Methods Expert) within the AI Methods group of AICS, focused on critical assessment of the model landscape, identifying opportunities for methodological innovation, and building AI approaches, algorithms, models, and workflows to maximize impact on biomedical research and drug discovery. Collaborates with AI researchers, data scientists, SMEs, and BR scientists to accelerate drug discovery and improve efficiency, while ensuring AI advancements address real biomedical questions.

Responsibilities

• Work with a team of AI researchers, data scientists and SMEs with core domain expertise to develop and deliver focused, robust, performant AI algorithms and solutions to accelerate drug discovery.
• Stay informed about the latest AI methods, understand the problem domain, and identify potential areas for AI innovation across the drug discovery pipeline.
• Promote awareness of advancements in AI methods for addressing key research questions within biomedical research.
• Fluently adopt Engineering and Product Development resources to ensure the adoption of AI solutions.
• Help position AI-aided drug discovery contributions to support BR‚Äôs portfolio, enable new therapeutic discoveries, shorten cycle times, and increase efficiency.
• Collaborate cross-functionally to translate model outputs into actionable hypotheses and guide experimental design.

Qualifications

• Required: A deep curiosity and passion for biomedical sciences driven therapeutic discovery.
• Required: A deep understanding and ability to explain the technical concepts underlying ML approaches.
• Required: 4+ years of significant experience in innovation, development, deployment, and continuous support of Machine Learning and modeling.
• Required: Wide exposure to representation learning, deep generative modeling, probabilistic reasoning, and explainability approaches.
• Required: Strong hands-on coding proficiency in Python and deep learning frameworks.
• Required: Strong understanding and experience with version control systems (e.g., GitHub, git, Subversion, Bitbucket).
• Required: Experience in large-scale model training, distributed computation, and foundation model adaptation.
• Required: Publications, patents, or open-source contributions demonstrating machine learning innovation and expertise.
• Required: Experience applying ML to functional areas of core drug discovery (e.g., target identification, computational chemistry, protein structure modelling and design, translational medicine) is a plus.
• Required: Expertise in bringing advanced analytics insights and actions to a large research organization.
• Required: Passion for understanding emerging technologies with pragmatic insight into their business integration.
• Required: Ability to balance requirements, manage expectations, and drive results in complex matrixed teams.

Skills

• Transformer architectures (text, vision, DNA/amino-acid sequences, gene expression vectors); long-context transformers; diffusion models; energy-based models; multimodal architectures; graph neural networks; geometric deep learning.
• Bayesian optimization; reinforcement learning; active learning; variational inference; self-supervised learning; contrastive learning; domain adaptation; test-time adaptation.
• Experience with translating model outputs into actionable hypotheses and guiding experimental design.

Education

• Education details are not explicitly provided in the source content.

Additional Requirements

• Travel and physical demands are not specified as essential in the source content.