---
title: 'ML & Molecular Simulation Scientist at Genesis Molecular AI'
canonical: 'https://feeny.ai/job/ml-molecular-simulation-scientist-genesis-molecular-ai-san-mateo-z7qx94p0vrzb'
type: 'job'
last_seen: '2026-09-07'
---

# ML & Molecular Simulation Scientist at Genesis Molecular AI

- **Company:** Genesis Molecular AI
- **Location:** San Mateo, CA
- **Employment:** full-time
- **Posted:** 2026-06-12
- **Last confirmed live:** 2026-09-07
- **Apply:** https://jobs.ashbyhq.com/genesis-molecular-ai/6e9c55b1-73b6-4881-8b7a-f7838626e17d

## Job description

## About the Team

At Genesis Molecular AI, we're a tight-knit team 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 don't just apply machine learning to biology – we conduct fundamental research at the intersection of machine learning, physics, and computational chemistry, pushing the boundaries of each field.

At Genesis, simulation and machine learning aren't separate disciplines: they're deeply integrated, and the scientists who do this work 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 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.

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 http://genesis.ml 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 into industry-leading models to generate and optimize drug molecules, including the breakthrough generative diffusion model Pearl https://www.businesswire.com/news/home/20251028030745/en/Genesis-Molecular-AI-Unveils-Pearl-a-Field-Leading-Foundation-Model-that-Achieves-Unprecedented-Performance-in-Drug-Protein-Structure-Prediction for structure prediction. Genesis is backed by premier AI and life science investors, including a16z, NVIDIA, Rock Springs Capital, Menlo Ventures, T. Rowe Price, Fidelity, and Radical Ventures. Genesis has also signed category-leading AI-pharma deals, the most recent of which was a significant expansion with Incyte (see coverage in Forbes https://www.forbes.com/sites/innovationrx/2026/05/20/inside-incytes-120-million-ai-for-drug-development-deal/ and GEN https://www.genengnews.com/topics/artificial-intelligence/small-molecules-to-big-partnership-incyte-genesis-expand-ai-collaboration-to-1b/) with a total potential deal value of several billion dollars.

Genesis is headquartered in San Mateo, CA, with a fully integrated laboratory in San Diego. We are proud to be an inclusive workplace and an Equal Opportunity Employer.

## About Genesis Molecular AI

## Company Overview
- **One-liner**: Genesis Molecular AI combines frontier AI research with deep drug discovery expertise to develop a proprietary platform (GEMS) that accelerates the design and optimization of small molecule medicines.
- **Entity Type**: Private (Series B; raised over $300M in total funding)
- **Headquarters**: Burlingame, California, United States (with a fully integrated laboratory in San Diego and offices in New York)
- **Founded**: Not publicly disclosed (first major funding round – $200M Series B – closed in 2023)
- **Founders**: Evan Feinberg Ph.D. (CEO, Co-founder) and Sergey Edunov (Co-founder); other key leaders include Will McCarthy (CTO) and Shifeng Pan Ph.D. (COO)

## Core Business
- **Primary Industry**: Biotechnology – AI-powered small molecule drug discovery.
- **Target Customers**: Large pharmaceutical partners (B2B) via platform collaborations, plus an internal pipeline of wholly owned drug programs.
- **Mission/Purpose**: “Solving the hardest problems in drug discovery to change the landscape of medicine” by creating and scaling domain-specific AI models that integrate physics, chemistry, biology, and software.

## Products & Services
- **[GEMS Platform](https://www.genesis.ml/)**: An AI operating system for molecular design. GEMS (Genesis Exploration of Molecular Space) integrates generative and predictive models to accelerate hit identification, lead optimization, and candidate selection. It is deployed for internal pipeline programs and major pharma collaborations.
- **[Pearl Foundation Model](https://www.genesis.ml/about)**: A generative foundation model for biomolecular structure prediction (protein-ligand) that demonstrated field-leading performance. Built in collaboration with NVIDIA and trained on large-scale synthetic data.
- **Internal Drug Pipeline**: Small molecule inhibitors targeting oncology (e.g., pan-mutant allosteric PIK3CA inhibitors) and immunology (e.g., inflammatory signaling pathways). Programs are in discovery stage approaching development candidate nomination.
- **Platform Partnership Model**: “Forward-deployed engineers and scientists” work alongside pharma partners (Gilead, Incyte) to apply GEMS to their targets, with deal structures that include upfront payments, milestones, and data sharing that feeds back into model training.

## Market Standing
- **Valuation/Market Cap**: Not disclosed.
- **Key Metric**: Total funding raised >$300M (including $200M Series B in 2023 co-led by a16z, with participation from Fidelity, BlackRock, NVIDIA’s NVentures, Rock Springs Capital, T. Rowe Price, Radical Ventures, Menlo Ventures).
- **Notable Investors/Partners**: Andreessen Horowitz, Fidelity, BlackRock, NVIDIA (NVentures), Rock Springs Capital, T. Rowe Price, Radical Ventures, Menlo Ventures. Pharma partners: Gilead ($35M upfront, 2024), Incyte ($150M total upfront including equity investment, 2025–2026).
- **Growth Signals**: 125 employees (+26.4% YoY); operates in 7 countries; LinkedIn following grew 41.9% in the past year; Pearl model launched to strong benchmarking results; Incyte expanded collaboration with at least 5 additional targets and data for model training.

## Competitive Advantages
- **Integration of disciplines**: The company pairs leading AI researchers with world-class drug hunters, co-creating models and medicines in iterative loops.
- **Wet-lab flywheel**: In-house lab in San Diego generates experimental data that closes the loop for model training, prediction, and validation – a virtuous cycle that strengthens the platform with every program.
- **Partnership data advantage**: Deals with Gilead and Incyte provide significant proprietary experimental data (e.g., Incyte data for training GEMS), creating a moat that is hard for pure-play AI companies to replicate.
- **Domain-specific foundation models**: Models like Pearl are purpose-built for small molecule drug discovery, unlike general-purpose generative AI.

## Strategic Focus
- **Advancing internal pipeline**: Moving lead programs in oncology (PIK3CA, apoptosis) and immunology toward development candidate nomination.
- **Scaling platform partnerships**: Expanding existing collaborations and signing new ones to broaden the impact of GEMS and generate more training data.
- **Continuous model improvement**: Investing in next-generation foundation models (e.g., Pearl) and leveraging partner data to stay at the frontier of molecular AI.
- **Hiring across AI and drug discovery**: Open roles for machine learning research scientists, software engineers, computational chemists, medicinal chemists, and biologists indicate a focus on scaling both the tech and biology teams.

## Why Work Here
- **Culture**: Described as a “meritocracy of ideas” where the strongest ideas win through evidence and reasoning, not hierarchy. Values include ownership, experimentation, candor with kindness, curiosity, and collaboration across disciplines.
- **Work Environment**: Flexible work environment with offices in Burlingame, San Diego, and New York. Daily meals and snacks provided in the office. Ergonomic consulting and equipment available.
- **Compensation & Benefits**: Highly competitive compensation including base salary, bonus, and equity. Comprehensive health, dental, and vision insurance. Open PTO policy, parental leave, 401(k) retirement savings, financial planning resources.
- **Impact**: Employees work on some of the hardest problems in drug discovery – applying cutting-edge AI to targets that are canonically undruggable, with the potential to create new treatment options for severe diseases.
- **Team Composition**: The company actively hires across AI research, software engineering, molecular simulation, and drug discovery disciplines, fostering an interdisciplinary environment where researchers and drug hunters co-create daily.

## Sources
1. [genesis.ml](https://www.genesis.ml/)
2. [genesis.ml/about](https://www.genesis.ml/about)
3. [genesis.ml/careers](https://www.genesis.ml/careers)
4. [genesis.ml/partners-pipeline](https://www.genesis.ml/partners-pipeline)
5. [LinkedIn - Genesis Molecular AI](https://www.linkedin.com/company/genesis-molecular-ai)

## Other roles at Genesis Molecular AI

- [Contract Recruiting Coordinator](https://feeny.ai/job/contract-recruiting-coordinator-genesis-molecular-ai-new-york-jp3t2jz8ff95) — New York, NY / San Francisco, CA
- [Manager, New Product Planning](https://feeny.ai/job/manager-new-product-planning-genesis-molecular-ai-san-diego-s9gybtg3hfg8) — San Diego, CA
- [Applied ML Scientist (Staff / Principal)](https://feeny.ai/job/applied-ml-scientist-staff-principal-genesis-molecular-ai-san-mateo-ypytjf6pxab2) — San Mateo, CA
- [Staff Technical Recruiter](https://feeny.ai/job/staff-technical-recruiter-genesis-molecular-ai-san-mateo-t76m4py49wex) — San Mateo, CA
- [Fullstack Software Engineer (Senior / Staff)](https://feeny.ai/job/fullstack-software-engineer-senior-staff-genesis-molecular-ai-san-mateo-pap6516adm11) — San Mateo, CA
- [Software Engineer - Core Infrastructure](https://feeny.ai/job/software-engineer-core-infrastructure-genesis-molecular-ai-san-mateo-sgbtcj4j76d4) — San Mateo, CA
- [Machine Learning Infrastructure Engineer](https://feeny.ai/job/machine-learning-infrastructure-engineer-genesis-molecular-ai-san-mateo-a012z1zdp33y) — San Mateo, CA
- [Product Management Lead](https://feeny.ai/job/product-management-lead-genesis-molecular-ai-san-mateo-qv16q5s8c4q2) — San Mateo, CA
- [ML Research Scientist, Foundation Models (Senior / Staff / Principal)](https://feeny.ai/job/ml-research-scientist-foundation-models-senior-staff-principal-genesis-gnpr61e1bf7n) — San Mateo, CA
- [ML Research Engineer, Foundation Models (Senior / Staff / Principal)](https://feeny.ai/job/ml-research-engineer-foundation-models-senior-staff-principal-genesis-molecular-7ky449yvkh2y) — San Mateo, CA