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From Home Molecular Simulation Jobs (NOW HIRING)

Deep Origin

Senior Scientific Developer

... molecular simulation stack. You will have ownership of key scientific capabilities from design through deployment, with a high level of independence and direct impact on what researchers can run on ...

State of South Carolina

Post Doctoral

Columbia, SC

$45K - $61K/yr

About UofSC From the Upstate to the Lowcountry, the University of South Carolina system is ... Experience with molecular simulation, machine learning, or related computational methods for ...

Achira

CADD / Application Scientist

New York, NY · On-site

$164K - $259K/yr

... for molecular simulation that can make the chemical and biological systems behind drug discovery more learnable, predictable, and designable. You will be part of the journey from our first protein ...

State of South Carolina

Post Doctoral

Columbia, SC · On-site

$45K - $61K/yr

About UofSC From the Upstate to the Lowcountry, the University of South Carolina system is ... using molecular simulation and artificial intelligence. The position will support startup-funded ...

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How much do from home molecular simulation jobs pay per year?

As of Aug 2, 2026, the average yearly pay for from home molecular simulation in the United States is $80,687.00, according to ZipRecruiter salary data. Most workers in this role earn between $69,000.00 and $98,500.00 per year, depending on experience, location, and employer.
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ML & Molecular Simulation Scientist

Genesis Molecular AI

San Mateo, CA • On-site

Full-time

Re-posted 21 days ago

Job description

Job Summary:
Genesis Molecular AI is pioneering foundation models for molecular AI to unlock a new era of drug design and development. They are seeking a ML & Molecular Simulation Scientist to develop and apply methods at the intersection of 3D molecular simulation and machine learning, contributing to impactful drug discovery programs.
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 our 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:
Required:
• 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
Preferred:
• Familiarity with cheminformatics and ADMET property prediction
• Contributions to open-source simulation or ML tooling
Company:
Genesis Therapeutics unifies AI and biotech to accelerate the discovery of new medicines. Founded in 2019, the company is headquartered in South San Francisco, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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