ML & Molecular simulation Scientist

What if your expertise in machine learning and molecular modelling could accelerate the development of tomorrow’s life-changing medicines? R&D Partners is seeking an ML & Molecular Simulation Scientist to develop and apply cutting-edge methods at the intersection of 3D molecular simulation and machine learning. This role offers the opportunity to contribute directly to drug discovery programs by integrating physics-based simulations with modern AI techniques. Applicants must have legal authorization to work in the United States.

Responsibilities

• Build and apply ML models informed by 3D structural data, including geometric deep learning, equivariant neural networks, and diffusion-based generative models for molecular design and property prediction.
• Integrate physics-based and ML-driven approaches, combining force field methods, quantum chemistry, and structure-based design to enhance accuracy and throughput.
• Develop and apply simulation methods such as molecular dynamics (MD), enhanced sampling, metadynamics, replica exchange, umbrella sampling, and free energy calculations (FEP-TI) to support active drug discovery programs.
• Contribute to platform development by improving generative AI and scoring capabilities, focusing on 3D methods and next-gen force fields.
• Collaborate with CADD and discovery scientists to apply computational methods across the drug discovery pipeline, from target structure analysis to lead optimization.
• Stay current with advancements in geometric ML, biomolecular simulation, and computational drug design, implementing and adapting methods from the latest literature.
• Communicate scientific results clearly to multidisciplinary teams, including experimental chemists and biologists.

Requirements

• Education: PhD preferred in computer science, machine learning, chemical engineering, biophysics, physics, or a closely related field. Postdoctoral or industry experience is a plus.
• Machine Learning Expertise: Practical experience with 3D machine learning, geometric deep learning, graph neural networks, equivariant architectures (e.g., SE3/E3 networks), or diffusion models applied to molecular data.
• Molecular Simulation Expertise: Deep hands-on experience with MD, enhanced sampling, and/or free energy methods using tools like GROMACS, AMBER, OpenMM, or NAMD.
• Drug Design Knowledge: Familiarity with structure-based drug design workflows, docking, binding site analysis, and protein-ligand interaction modeling using tools like MOE or PyMOL.
• Technical Proficiency: Strong skills in Python and scientific computing libraries (e.g., PyTorch, JAX, NumPy, MDAnalysis, RDKit) and comfort with HPC environments and scripting for large-scale simulation workflows.
• Track Record: Demonstrated success in applying computational methods to real scientific problems through publications, open-source contributions, or industry impact.
• Soft Skills: Collaborative, curious, and able to balance rigorous method development with fast-paced discovery work.

Nice to Have

• Familiarity with cheminformatics and ADMET property prediction.
• Contributions to open-source simulation or ML tooling.

Pay

$190,000 - $200,000 per annum