[Hiring] Computational Chemistry & AI/ML Scientist @Marlabs

Role Description

As a Computational Chemistry & AI/ML Scientist, you will leverage advanced computational chemistry methodologies, machine learning, and generative AI technologies to accelerate molecular design, property prediction, and drug discovery research. You will collaborate closely with multidisciplinary scientific teams to develop and deploy cutting-edge AI/ML models, molecular simulation workflows, and computational platforms that support lead discovery, optimization, and predictive modeling initiatives. The ideal candidate combines deep expertise in computational chemistry with hands-on experience in modern AI/ML frameworks and molecular modeling technologies.

Day-to-Day

• Design, develop, and deploy AI/ML models, including graph neural networks, transformers, generative models, and Bayesian optimization techniques for molecular property prediction and molecular design.
• Apply computational chemistry methodologies such as molecular modeling, molecular dynamics simulations, quantum mechanical calculations, and structure-based modeling to support research initiatives.
• Build and optimize generative AI workflows for de novo molecular design, scaffold hopping, lead optimization, and multi-parameter optimization challenges.
• Develop and maintain cheminformatics pipelines, molecular datasets, and model validation frameworks using Python and modern machine learning libraries.
• Collaborate with scientific, computational, and data science teams to evaluate model performance, generate actionable insights, and support decision-making throughout the drug discovery process.
• Deploy scalable AI and computational chemistry workflows within cloud and high-performance computing environments while supporting continuous improvement of modeling and simulation capabilities.

Qualifications

• Ph.D. in Computational Chemistry, Cheminformatics, Chemistry, Computer Science, Data Science, or a related field, with 1-3 years of industry or academic research experience in computational chemistry, AI/ML, or molecular modeling.
• Strong expertise in deep learning and AI/ML frameworks, including PyTorch, JAX, graph neural networks, transformers, generative AI models, and geometric deep learning approaches for molecular applications.
• Hands-on experience developing and deploying AI/ML models for molecular property prediction, molecular generation, lead optimization, drug discovery, or related scientific applications.
• Strong computational chemistry background, including molecular modeling, molecular dynamics simulations, quantum chemistry, and experience with tools such as Schrödinger Suite, MOE, AMBER, GROMACS, NAMD, PyMOL, or VMD.
• Proficiency in Python and scientific computing ecosystems, including RDKit, Scikit-learn, Pandas, NumPy, TensorFlow, PyTorch, and Linux-based or high-performance computing environments.
• Strong analytical, scientific communication, and problem-solving skills, with the ability to work independently and collaborate effectively across multidisciplinary research teams.
• Preferred experience in pharmaceutical, biotechnology, drug discovery, or life sciences research environments.