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Computational Drug Design Jobs (NOW HIRING)

Namely

Head of Discovery

Manhattan, NY · On-site

$100 - $150/hr

  • Medical

  • Dental

  • Vision

You have experience with AI‑driven drug discovery or computational drug design tools * You have experience managing CRO relationships for synthesis and biological testing * You have experience ...

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Computational Drug Design information

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How much do computational drug design jobs pay per hour?

As of Aug 16, 2026, the average hourly pay for computational drug design in the United States is $54.93, according to ZipRecruiter salary data. Most workers in this role earn between $46.88 and $73.56 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a computational drug design scientist?

To thrive as a Computational Drug Design scientist, you need a strong background in chemistry, biology, and computer science, typically supported by an advanced degree (e.g., PhD) in a related field. Proficiency with molecular modeling software, cheminformatics tools, and programming languages such as Python or R is essential, along with familiarity with databases like PDB and software such as Schrödinger or MOE. Strong analytical thinking, problem-solving abilities, and effective communication skills help translate computational findings into actionable insights for multidisciplinary teams. These competencies are crucial for efficiently identifying promising drug candidates and supporting data-driven decision-making in pharmaceutical research.

What is computational drug design?

Computational drug design is the use of computer-based methods and simulations to discover, develop, and optimize new pharmaceutical compounds. This field combines chemistry, biology, and computer science to model how potential drug molecules interact with biological targets, such as proteins or enzymes. Techniques like molecular docking, virtual screening, and molecular dynamics are commonly used to predict the efficacy and safety of new drugs before laboratory testing. By leveraging computational tools, researchers can significantly speed up the drug discovery process and reduce costs.

What is the difference between Computational Drug Design vs Medicinal Chemist?

AspectComputational Drug DesignMedicinal Chemist
Required CredentialsDegree in Chemistry, Bioinformatics, or related field; strong computational skillsDegree in Chemistry, Organic Chemistry, or related field; laboratory experience
Work EnvironmentResearch labs, pharmaceutical companies, biotech firms; primarily computer-basedLaboratories, pharmaceutical companies; hands-on chemical synthesis and analysis
Industry UsageDrug discovery, virtual screening, molecular modeling

Computational Drug Design focuses on using computer simulations and modeling to identify potential drug candidates, while Medicinal Chemists are involved in synthesizing and testing chemical compounds in the lab. Both roles are essential in the drug development process but differ in their methods and work environments.

What are some common challenges faced in a computational drug design role, and how can they be addressed?

Professionals in Computational Drug Design often encounter challenges such as managing large and complex datasets, integrating diverse software tools, and ensuring accurate modeling of biological systems. Addressing these challenges typically involves continuous learning to stay updated with the latest algorithms and software, collaborating closely with experimental scientists, and developing strong data management practices. Effective communication and teamwork are also essential, as the role frequently involves working in multidisciplinary teams to translate computational findings into actionable experimental strategies.
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Computational Drug Design jobs near you
Infographic showing various Computational Drug Design job openings in the United States as of August 2026, with employment types broken down into 1% Internship, 88% Full Time, 6% Part Time, and 5% Contract. Highlights an 84% Physical, 5% Hybrid, and 11% Remote job distribution, with an average salary of $114,249 per year, or $54.9 per hour.

ML & Molecular Simulation Scientist

Genesis Molecular AI

San Mateo, CA • On-site

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 5 days ago

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 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 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 and GEN) 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.

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