About this opportunity
Menlo Ventures lists this ML & Molecular Simulation Scientist opportunity in san mateo, California. Review the employer’s description below for duties, qualifications and application requirements.
Job description
About the Team
At Genesis Molecular AI, we are a tight‑knit group of deep learning researchers, computational scientists, and drug discovery pioneers united by a single mission: to develop the next generation of AI‑driven therapies for patients with severe diseases. We conduct fundamental research at the intersection of machine learning, physics, and computational chemistry, pushing the boundaries of each field. Simulation and machine learning are deeply integrated, and scientists who work here sit at the center of everything we build. You will work side by side with world‑class researchers across ML, chemistry, and biology, with access to large‑scale compute infrastructure and simulation pipelines, contributing to a platform where physics‑based methods and AI advance together.
About the Role
We are seeking an 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.
This is a role for someone who thrives at the intersection of computational science and machine learning: 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.
Some areas you may focus on
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
Who You Are
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
Nice to Have
Familiarity with cheminformatics and ADMET property prediction
Contributions to open‑source simulation or ML tooling
What We Offer
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
About Genesis Molecular AI
Genesis Molecular AI is pioneering foundation models for molecular AI to unlock a new era of drug design and development. Our generative and predictive AI platform, GEMS (Genesis Exploration of Molecular Space), integrates AI and physics to generate and optimize drug molecules, including the breakthrough generative diffusion model Pearl for structure prediction. Genesis is proud to be an inclusive workplace and an Equal Opportunity Employer.
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Worksite address
san mateo, CA, 94409, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.