ML & Molecular Simulation Scientist
San Mateo, CAFull-timePosted 3mo ago
About the role
Role focused on developing and applying methods at the intersection of 3D molecular simulation and machine learning to advance drug discovery, combining physics-based methods with ML and collaborating across CADD and discovery teams.
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 + 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 spanning 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, improving generative AI and scoring capabilities, focusing on 3D methods; strengthen ML and physics-based scoring functions (and their intersection), build next-gen force fields
- Work directly with CADD and discovery scientists to apply computational methods across the drug discovery pipeline, from target structure analysis through lead optimization
- Stay current with the field, implementing and adapting methods from the latest literature in geometric ML, biomolecular simulation, and computational drug design
- Communicate scientific results clearly to multidisciplinary teams, including experimental chemists and biologists
Qualifications
- Practical experience with 3D machine learning – geometric deep learning, graph neural networks, equivariant architectures (e.g., SE(3)/E(3) networks), or diffusion models applied to molecular data
- PhD (preferred) in computer science, machine learning, chemical engineering, biophysics, physics, or a closely related field; postdoctoral 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, protein-ligand interaction modeling using tools such as MOE, or PyMOL
- Proficiency in Python and scientific computing libraries (PyTorch, JAX, NumPy, MDAnalysis, RDKit); comfort with HPC environments and scripting for large-scale simulation workflows
- A track record of applying computational methods to real scientific problems, demonstrated through publications, open-source contributions, or industry impact
- Collaborative, curious, and able to move between rigorous method development and fast-paced discovery work
Skills
Required:3D machine learningMolecular simulation
Benefits
- Highly competitive compensation including base, bonus, and equity
- Comprehensive health, dental, and vision insurance (fully covered for employees)
- Stock option eligibility
- 401(k) plan
- Open PTO policy
- Paid company holidays
- Daily meals and snacks in the office
- Flexible work environment
Last updated Jul 20, 2026. Interested? Apply on the company site.
About Genesis Molecular AI
The frontier AI for drug discovery
Genesis pairs frontier AI with world-class drug hunters to discover small molecule medicines. Built on Pearl and GEMS. Partnered with Gilead and Incyte.
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