[Hiring] AI Scientist – Molecular Machine Learning @Marlabs

Role Description

As an AI Scientist – Molecular Machine Learning, you will design and develop advanced AI/ML models that accelerate molecular property prediction, molecular generation, and drug discovery. You will apply deep learning, graph neural networks, geometric learning, transformers, and generative AI to complex molecular and chemical datasets, partnering closely with computational chemists and drug discovery scientists to solve high-impact scientific challenges.

Day-to-Day

• Design, develop, train, and evaluate deep learning models for molecular property prediction, molecular generation, and drug discovery applications.
• Build molecular AI solutions using Graph Neural Networks (GNNs), Graph Transformers, geometric deep learning, and generative modeling techniques.
• Develop scalable AI/ML workflows using Python, PyTorch and/or JAX, optimizing models for accuracy, efficiency, and scientific relevance.
• Build and manage molecular datasets and cheminformatics workflows using RDKit and related tools, including molecular representation, preprocessing, feature generation, and model-ready dataset development.
• Develop generative modeling approaches for de novo molecular design, lead optimization, and molecular exploration, leveraging techniques such as diffusion models, flow matching, reinforcement learning, or related architectures.
• Collaborate with computational chemists, medicinal chemists, and drug discovery scientists to translate scientific problems into AI/ML approaches and generate actionable insights for molecular discovery programs.
• Evaluate and continuously improve molecular ML models using rigorous experimentation, validation, benchmarking, and reproducible research practices.

Qualifications

• Ph.D. in Computer Science, AI/ML, Computational Chemistry, Cheminformatics, Data Science, Physics, or a related technical or scientific discipline, with relevant experience applying AI/ML to molecular or scientific problems.
• Strong hands-on expertise with PyTorch and/or JAX, including developing, training, evaluating, and optimizing deep learning models.
• Demonstrated experience with Graph Neural Networks, Graph Transformers, geometric deep learning, and generative AI, particularly for molecular representation, property prediction, or molecular design.
• Strong Python programming and cheminformatics experience, including RDKit or comparable toolkits, with experience working with complex molecular datasets.
• Experience applying AI/ML to molecular property prediction, molecular generation, drug discovery, ADMET modeling, lead optimization, or related molecular science applications.
• Experience working within Linux and high-performance computing environments, with exposure to tools such as Schrödinger, MOE, GROMACS, OpenMM, or other molecular modeling platforms considered valuable.