Transform drug discovery and preclinical development by inventing, prototyping, and applying advanced Machine Learning (ML) methods, particularly in generative modeling and related areas, to expand capabilities in designing, prioritizing, and characterizing novel therapeutic candidates.
Requirements
- Proficiency in core ML/statistics topics such as probability, statistical inference, optimization, discrete math/algorithms, and/or probabilistic modeling
- Strong programming skills in Python and experience with modern ML frameworks (e.g., PyTorch, TensorFlow)
- Experience with molecular representations (e.g., SMILES, graphs), generative models (e.g., diffusion models, VAEs, flow models), and sequence/structure models (e.g., transformers, GNNs, protein or RNA models)
- Familiarity with cheminformatics/biophysics toolkits (e.g., RDKit), docking or molecular simulation, ADMET modeling, or DMPK-relevant endpoints
- Practical experience with experimental design, active learning, uncertainty quantification, or multi-objective optimization
- Software engineering best practices (Git, testing, containers), and experience working with large datasets and cloud/GPU environments
Responsibilities
- Conduct original research to develop state-of-the-art AI/Machine learning methods for drug discovery (e.g., molecular generative models, multi-objective optimization, property prediction, active learning)
- Design and execute experiments, analyze results rigorously, and iterate rapidly on model architectures and training strategies
- Build robust, reproducible code and workflows; contribute to shared libraries and documentation
- Collaborate with chemists, biologists, Pharmacokinetics, Dynamics, Metabolism, and Bioanalytics (PDMB) scientists, and data/ML engineers to translate methods into impactful applications
- Communicate findings through internal presentations and peer-reviewed publications; present at conferences and workshops
Other
- Demonstrated research excellence and problem-solving ability; strong motivation to learn, innovate, and deliver
- Track record of publications and/or presentations in ML, computational chemistry/biology, or related fields
- Excellent collaboration and communication skills; proven ability to work in cross-functional teams
- 10% Travel Requirements
- Hybrid Flexible Work Arrangements
