AI Research Scientist —Generative AI for Materials Discovery

Responsibilities

• Develop, train, and deploy generative models (diffusion models, flow matching, variational autoencoders, transformer-based architectures) for molecular and crystal structure generation, property-conditioned design, and crystal structure prediction (CSP)
• Design and implement reinforcement learning and alignment strategies (e.g., physics-informed reward signals from machine-learned interatomic potentials) to steer generative models toward physically stable and synthesizable candidates
• Build foundational models and scalable pretraining pipelines that unify generative and predictive learning across molecules and crystalline materials, handling both discrete atom types and continuous 3D geometries
• Collaborate closely with computational chemists to integrate first-principles calculations (DFT, force fields), molecular dynamics simulations, and domain-specific constraints into generative workflows
• Partner with AI agent scientists to embed generative molecular design capabilities into LLM-based multi-agent systems, enabling closed-loop autonomous experiment planning, candidate generation, and decision making
• Curate, preprocess, and manage large-scale molecular and crystal structure datasets for model training and benchmarking
• Establish rigorous evaluation frameworks — measuring validity, novelty, uniqueness, stability, and synthesizability of generated structures — and benchmark against state-of-the-art methods
• Contribute to the architecture and roadmap of the autonomous materials-discovery platform, ensuring generative design modules interface seamlessly with robotic workcells, characterization instruments, and data infrastructure

Requirements

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• Ph.D. degree in Machine Learning, Computational Chemistry, Materials Science, Chemical Engineering, Physics, or a closely related technical field
• 3+ years of research experience in generative modeling applied to molecular systems, crystal structures, or materials science (academic or industry)
• Familiarity with large-scale molecular and crystal databases and data processing pipelines for chemical data
• Demonstrated expertise in deep generative models — including diffusion models, flow matching / continuous normalizing flows, variational autoencoders, or autoregressive models — with applications to 3D molecular or crystal structure generation
• Programming proficiency in Python with hands-on experience in PyTorch or JAX
• Proficiency in building, training, and evaluating large-scale deep learning models
• Track record of first-author publications in top-tier ML or computational chemistry venues (e.g., NeurIPS, ICML, ICLR, JACS, Nature Computational Science, Digital Discovery)
• Solid understanding of crystallography fundamentals— and molecular representations (molecular graphs, SMILES, 3D conformers)