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Polymer Computational Scientist Jobs (NOW HIRING)

MonoSol

Senior Scientist Process Modeling

Chicago, IL · On-site

$94K - $128K/yr

... Polymer Science, Physics, or a related technical field; Master's or PhD strongly preferred Experience * 5+ years applying process modeling, simulation, or computational engineering to real-world ...

MonoSol

Senior Scientist Process Modeling

Chicago, IL · On-site

$94K - $128K/yr

... Polymer Science, Physics, or a related technical field; Master's or PhD strongly preferred Experience * 5+ years applying process modeling, simulation, or computational engineering to real-world ...

MonoSol

$67K - $92K/yr

... Polymer Science, Physics, or a related technical field; Master's or PhD strongly preferred Experience * 5+ years applying process modeling, simulation, or computational engineering to real-world ...

Hexion

Applied ML Scientist - Chemistry

Columbus, OH · On-site

Minimum Qualifications * Bachelor's degree in Computer Science, Data Science, Computational ... Domain knowledge in chemistry, preferably polymer chemistry. * Demonstrated ability to develop ...

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How much do polymer computational scientist jobs pay per year?

As of Sep 13, 2026, the average yearly pay for polymer computational scientist in the United States is $111,343.00, according to ZipRecruiter salary data. Most workers in this role earn between $94,500.00 and $137,000.00 per year, depending on experience, location, and employer.

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Infographic showing various Polymer Computational Scientist job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 87% Full Time, 7% Part Time, and 5% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $111,343 per year, or $53.5 per hour.

Postdoctoral Research Associate - Isayev Lab

Pittsburgh, PA • On-site

Carnegie Mellon University
Colleges, Universities, and Professional Schools • 1 - 10 employees
Learn more about Carnegie Mellon Universityexternal

Full-time

Re-posted 16 days ago

Carnegie Mellon University rating

8.8

Company rating: 8.8 out of 10

Based on 25 frontline employees who took The Breakroom Quiz

Job description

Description
The Isayev Lab at Carnegie Mellon University invites applications for a postdoctoral researcher to lead projects at the interface of computational chemistry, machine learning, reaction mechanism elucidation, and automated molecular discovery. The position is ideal for a candidate who wants to turn deep mechanistic understanding into predictive models and closed-loop discovery workflows.
Our lab develops and applies machine learning methods for computational chemistry, materials science, and molecular discovery, including transferable neural network potentials, generative molecular design, and experiment-automation workflows. The postdoc will work in a collaborative CMU environment spanning computational chemistry, AI, automated experimentation, polymer chemistry, and catalysis.
Research directions may include:
Developing automated DFT / ML workflows for mechanistic studies of photoredox, organometallic, and radical catalytic reactions.
Building predictive models that connect quantum-chemical descriptors, catalyst structure, substrate scope, selectivity, and reaction performance.
Applying AIMNet2 and related ML/QM methods to accelerate conformer search, reaction-path exploration, catalyst screening, and high-throughput mechanistic modeling.
Designing closed-loop computational-experimental campaigns for transition metal catalysis, polymer synthesis, and related catalytic transformations.
Creating reusable, open, well-documented software workflows for reaction data generation, curation, featurization, and model deployment.
Collaborating with experimental groups at CMU and external partners to convert mechanistic hypotheses into experimentally testable predictions.
Qualifications
Desired background:
Ph.D. in chemistry, chemical engineering, materials science, or a related field.
Strong experience in computational reaction mechanisms, especially DFT studies of organic, organometallic, photoredox, radical, or homogeneous catalytic systems.
Fluency with Python and modern scientific computing workflows; experience with Git, HPC clusters, SLURM, Gaussian, ORCA, Q-Chem, xTB, RDKit, ASE, or related tools is highly valued.
Interest in machine learning, statistical modeling, active learning, descriptor development, or data-driven reaction prediction.
Ability to work closely with experimental collaborators and communicate mechanistic insight clearly.
Application Instructions
Applications, including a cover letter and a curriculum vitae indicating your interest and relevant training should be submitted electronically via Interfolio.

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