Job Description

• 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

• Ph.D. (or completion within six months) in Computer Science, Statistics, Physics, Applied Mathematics, Bioinformatics, Computational Biology, Chem/Informatics, Engineering, or a related field

• Demonstrated research excellence and problem-solving ability; strong motivation to learn, innovate, and deliver
• 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)
• 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

• 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

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