ML & Molecular Simulation Scientist

Menlo Ventures; San Mateo (CA); On-site; USD 110,000 - 150,000; Full time

Benefits offered by this job

Highly competitive compensation

Comprehensive health, dental, and vision insurance

Stock option eligibility

401(k) plan

Open PTO policy

Paid company holidays

Daily meals and snacks in the office

Flexible work environment

Job summary

Alumni Ventures in San Mateo, California is seeking an ML & Molecular Simulation Scientist to develop innovative methods that bridge 3D molecular simulation with machine learning. The ideal candidate will work closely with leading researchers in a team-oriented environment, contributing to key drug discovery initiatives.

The role requires expertise in molecular simulation techniques and strong programming skills in Python. A competitive compensation package, including health benefits and flexible work options, is offered.

Qualifications

• Practical experience with geometric deep learning, graph neural networks, equivariant architectures.
• Deep hands-on expertise in molecular simulation including MD and enhanced sampling methods.
• Proficiency in Python and scientific computing libraries like PyTorch, NumPy, and RDKit.

Responsibilities

• Develop and apply methods at the intersection of molecular simulation and machine learning.
• Integrate various computational approaches for drug discovery programs.
• Collaborate with teams to move molecules from hit identification to lead optimization.

Skills

3D machine learning

molecular simulation

Python programming

scientific computing libraries

Education

PhD in a related field

Tools

GROMACS

AMBER

OpenMM

NAMD

PyMOL

MOE

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.