ML & Molecular simulation Scientist

R&D Partners; California (MO); On-site; USD 190,000 - 200,000; Full time

Job summary

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, shaping tomorrow's drug discovery programs.

You will build ML models from 3D structural data, integrate physics-based and ML approaches, run MD and enhanced sampling workflows, and collaborate with CADD and discovery scientists to accelerate lead optimization.

Qualifications

• PhD preferred in computer science, ML, chemical engineering, biophysics or physics.
• Strong background in 3D ML, geometric DL, and equivariant architectures for molecular data.
• Hands-on MD, enhanced sampling, and free energy methods with established tools.
• Familiarity with structure-based drug design workflows and related software.
• Proficiency in Python and scientific computing libraries; HPC scripting experience.
• Track record of publications, open-source contributions, or industry impact.
• Collaborative mindset and ability to work in fast-paced discovery projects.

Responsibilities

• Build and apply ML models informed by 3D structural data, including geometric DL and diffusion models.
• Integrate physics-based and ML-driven approaches to enhance accuracy and throughput.
• Develop and apply MD, enhanced sampling, and free energy calculations to support drug discovery.
• Improve generative AI and scoring capabilities for 3D methods and force fields.
• Collaborate with CADD and discovery scientists across the drug discovery pipeline.
• Stay current with geometric ML and computational drug design literature; communicate results clearly.

Skills

3D ML

Geometric DL

Graph neural networks

SE3/E3 networks

Diffusion models

Python

NumPy

MDAnalysis

HPC scripting

Collaborative

Education

PhD preferred in computer science / ML / chemical engineering / biophysics / physics

Tools

GROMACS

AMBER

OpenMM

NAMD

MOE

PyMOL

RDKit

MDAnalysis

Job description

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.

Key Skills and 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.

Compensation:

• $190 000 - $200 000.00 Per Annum

For more information, please contact Indre Semeskeviciute.

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