Principal Scientist/Associate Director, Machine Learning

About the Role:

We are seeking a high-impact Machine Learning Scientist to join our integrated discovery team. In this role, leading from the bench, you will enable the development, validation and deployment of state-of-the-art ML models to generate the quantitative predictions necessary to drive drug discovery. Beyond technical mastery, you will serve as a core strategic partner to medicinal chemists, computational chemists, and biologists, building models that move programs efficiently toward Go/No-Go decision points and candidate nomination.  This role may be responsible for the management and development of individual team members.

• Lead the application of Large Language Models (LLMs), co-folding algorithms, and generative chemistry techniques to design novel chemical matter aimed at hitting key program milestones, such as establishing selectivity windows and optimizing drug-like properties
• ML lead on project teams, collaborating intimately with medicinal chemists to refine SAR and with structural biologists to integrate co-folding and structure-based insights into ML workflows
• Data-Driven Decision Making: Synthesize complex ML outputs into clear, actionable design hypotheses that cross-functional scientific stakeholders can use to make high-stakes program decisions
• May be responsible for management and development of internal team members

• Ph.D. in Computational Chemistry, Computer Science, Machine Learning, or a related field
• Demonstrated expertise in statistics, probability theory, data modeling, machine learning algorithms, and the languages used to implement analytics solutions
• 4-7+ years of experience in a biotech or pharma setting performing ML support for small molecule drug discovery with clear evidence of impact on drug discovery programs
• Demonstrated success in a cross-functional environment, including biologists, structural biologists, medicinal and computational chemists, with specific examples of computational designs/algorithms/models that directly led to the achievement of program milestones
• Expert proficiency in Python and deep learning libraries (e.g., PyTorch, TensorFlow) is required. You must be able to build and maintain production-quality code and data pipelines

• Proven experience with protein-ligand co-folding models (e.g.,Boltz, OpenFold, AlphaFold, etc) and the ability to integrate these structural insights into broader ML discovery pipelines
• Expertise fine-tuning existing models with internally generated structural biology and biology data
• Expert-level knowledge of deep learning frameworks, specifically for affinity prediction, ADMET modeling, and the application of LLMs in a biological or chemical context

• A demonstrated track record of innovation in the ML/AI space, including developing and validating new architectures or novel applications of existing models to solve complex drug discovery problems
• Demonstrated expertise using small molecule drug discovery ML/AI tools (AlphaFold, Boltz, OpenFold, ChemProp, DeepChem, Reinvent, etc)  
• Expert level coding for ML tasks including knowledge of key packages (RDKit, scikit-learn, numpy, pandas, pytorch, DeepChem, polars, PyG/DGL).
• Strong interpersonal and communications skills in the "why" behind a design to a diverse scientific audience
• Experience mentoring and developing teams