ML & Molecular Simulation Scientist

About the role

We are seeking a ML & Molecular Simulation Scientist to develop and apply methods at the intersection of 3D molecular simulation and machine learning, and see those methods through to real impact in drug discovery programs. The role combines designing and running simulations, building ML models grounded in physical intuition, and collaborating directly with CADD and discovery teams to move molecules from hit identification to lead optimization.

Key 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 data‑driven approaches, combining force‑field methods, quantum chemistry, and structure‑based design with modern ML to improve accuracy and throughput.
• Develop and apply simulation methods such as molecular dynamics, enhanced sampling (metadynamics, replica exchange, umbrella sampling), and free‑energy calculations (FEP/TI) to support active drug‑discovery programs.
• Contribute to the GEMS platform by improving generative AI and scoring capabilities, strengthening ML‑physics scoring functions, and building next‑generation force fields.
• Work directly with CADD and discovery scientists across the drug‑discovery pipeline, from target structure analysis through lead optimization.

Required profile

• Practical experience with 3D machine learning – geometric deep learning, graph neural networks, equivariant architectures, or diffusion models applied to molecular data.
• PhD (preferred) in computer science, machine learning, chemical engineering, biophysics, physics, or a related field; post‑doctoral or industry experience is a plus.
• Deep hands‑on expertise in molecular simulation, including MD, enhanced sampling, and/or free‑energy methods using tools such as GROMACS, AMBER, OpenMM, or NAMD.
• Familiarity with structure‑based drug‑design workflows: docking, binding‑site analysis, and protein‑ligand interaction modeling using tools such as MOE or PyMOL.
• Proficiency in Python and scientific‑computing libraries (PyTorch, JAX, NumPy, MDAnalysis, RDKit) and comfort with HPC environments for large‑scale simulation workflows.

Required skills

• Python programming
• PyTorch, JAX, NumPy, MDAnalysis, RDKit
• GROMACS, AMBER, OpenMM, NAMD
• MOE, PyMOL
• Geometric deep learning, graph neural networks, equivariant neural networks, diffusion models

What we offer

• Highly competitive compensation including base salary, bonus, and equity.
• Comprehensive health, dental, and vision insurance (fully covered for employees).
• Stock option eligibility and 401(k) plan.
• Open PTO policy, paid company holidays, and daily meals and snacks in the office.
• Flexible work environment.