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Remote Computational Chemical Engineering Jobs (NOW HIRING)

SES

Computational Materials Scientist

Woburn, MA · On-site +1

$180K - $200K/yr

D. in Mechanical Engineering, Materials Science, Chemical Engineering, or a closely related computational/physics field. * Core Simulation Expertise: Deep and extensive experience in atomistic ...

The Pennsylvania State University

$91K - $137K/yr

Approval of remote and hybrid work is not guaranteed regardless of work location.For additional ... A master's degree in an advanced degree in chemistry, chemical engineering, toxicology, or a ...

Redfish Recruiting

Computational Chemist

Chicago, IL · On-site +1

You'll work closely with our AI team and the front-end developers to connect the dots and make our ... Responsibilities: - Provide expert feedback on chemical predictions and tool functionality ...

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How much do remote computational chemical engineering jobs pay per year?

As of Aug 21, 2026, the average yearly pay for remote computational chemical engineering in the United States is $135,168.00, according to ZipRecruiter salary data. Most workers in this role earn between $125,000.00 and $148,500.00 per year, depending on experience, location, and employer.

What is remote computational chemical engineering?

Remote computational chemical engineering is a field where engineers use computer simulations and mathematical models to design, analyze, and optimize chemical processes from a remote location. These professionals work with specialized software to predict the behavior of chemical systems, such as reaction kinetics, fluid dynamics, and material properties. By working remotely, they can collaborate with teams across the globe, contribute to research and development, and solve complex engineering problems without being physically present in a lab or office. This approach offers flexibility and access to a wider range of projects in academia, industry, and research organizations.

What are the key skills and qualifications needed to thrive as a remote computational chemical engineer?

To thrive as a Remote Computational Chemical Engineer, you need a solid background in chemical engineering, advanced mathematics, and computational modeling, often supported by a relevant degree such as a BS or MS in Chemical Engineering. Proficiency with simulation software (e.g., Aspen Plus, COMSOL Multiphysics), programming languages (such as Python or MATLAB), and familiarity with cloud collaboration tools are typically required. Strong problem-solving abilities, self-motivation, and effective communication are standout soft skills for remote collaboration and project management. These skills ensure the accurate modeling and optimization of chemical processes, efficient remote teamwork, and successful delivery of complex engineering solutions.

What are some common challenges faced by remote computational chemical engineers and how can they be addressed?

Remote computational chemical engineers often encounter challenges related to effective collaboration and communication with multidisciplinary teams, as projects can involve chemists, software developers, and project managers across different time zones. Managing complex simulations and large data sets securely from a remote environment also requires robust IT infrastructure and self-discipline. To address these challenges, it's helpful to establish clear communication protocols, leverage collaboration tools, and proactively schedule regular check-ins with team members. Additionally, remote engineers should ensure they have access to reliable computing resources and seek out opportunities for virtual training to stay updated with the latest software and modeling techniques.

What is the difference between Remote Computational Chemical Engineering vs Remote Process Engineer?

AspectRemote Computational Chemical EngineeringRemote Process Engineer
Required CredentialsBachelor's/Master's in Chemical Engineering, programming skillsBachelor's/Master's in Chemical or Mechanical Engineering, process knowledge
Work EnvironmentPrimarily computer-based, data analysis, modelingDesign, optimize, and troubleshoot industrial processes remotely
Industry UsageResearch, simulation, software developmentManufacturing, refining, chemical production
Common Search/ComparisonRemote Chemical Engineering roles involving computationRemote process optimization roles

Remote Computational Chemical Engineering focuses on modeling, simulation, and data analysis using programming skills, often in research or software development contexts. In contrast, Remote Process Engineer roles involve designing and optimizing chemical processes remotely within manufacturing or production environments. Both roles require chemical engineering credentials but differ in daily tasks and industry focus.

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Cities with the most Remote Computational Chemical Engineering job openings:

What are the most commonly searched types of Computational Chemical Engineering jobs?

The most popular types of Computational Chemical Engineering jobs are:

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Remote Computational Chemical Engineering jobs near you
Infographic showing various Remote Computational Chemical Engineering job openings in the United States as of August 2026, with employment types broken down into 92% Full Time, 4% Part Time, 3% Contract, and 1% Nights. Highlights an 85% Physical, 5% Hybrid, and 10% Remote job distribution, with an average salary of $135,168 per year, or $65 per hour.

Computational Materials Scientist

SES

Woburn, MA • On-site, Remote

$180K - $200K/yr

Full-time

Medical

Re-posted 20 days ago

Job description

SES AI Corp. (NYSE: SES) is dedicated to accelerating the world's energy transition through groundbreaking material discovery and advanced battery management. We are at the forefront of revolutionizing battery creation, pioneering the integration of cutting-edge machine learning into our research and development. Our AI-enhanced, high-energy-density and high-power-density Li-Metal and Li-ion batteries are unique; they are the first in the world to utilize electrolyte materials discovered by AI. This powerful combination of "AI for science" and material engineering enables batteries that can be used across various applications, including transportation (land and air), energy storage, robotics, and drones.
To learn more about us, please visit: www.ses.ai
What We Offer:
  • A highly competitive salary and robust benefits package, including comprehensive health coverage and an attractive equity/stock options program within our NYSE-listed company.
  • The opportunity to contribute directly to a meaningful scientific project-accelerating the global energy transition-with a clear and broad public impact.
  • Work in a dynamic, collaborative, and innovative environment at the intersection of AI and material science, driving the next generation of battery technology.
  • Significant opportunities for professional growth and career development as you work alongside leading experts in AI, R&D, and engineering.
  • Access to state-of-the-art facilities and proprietary technologies are used to discover and deploy AI-enhanced battery solutions.

What we Need:
The SES AI Prometheus team isseeking an exceptional Computational Materials Scientist to combine physics-based simulation (DFT, MD, quantum modeling) with AI-assisted material prediction to generate high-quality training data and accelerate materials discovery. This role is crucial for advancing our understanding of electrochemical energy materials at the atomic level. As a Computational Materials Scientist, you will be a core data-driven modeler responsible for executing and automating complex simulations.
Essential Duties and Responsibilities:
  • Atomistic Modeling & Simulation
  • Conduct and oversee DFT (Density Functional Theory), MD (Molecular Dynamics), and QM (Quantum Mechanics) simulations of battery components, including electrolytes, coatings, and electrodes.
  • Develop and refine ML-enhanced force fields and surrogate models to accelerate simulation time scales and enable multi-scale simulation efforts.
  • Apply expertise in atomistic simulation and quantum modeling to solve key challenges in electrochemical energy materials (e.g., batteries/fuel cells).
  • AI Data Generation & Prediction
  • Generate high-quality, structured simulation data to serve as training sets for AI property prediction models and material screening modules.
  • Contribute to the development of battery domain LLM features and advanced property-prediction models.
  • Automate complex simulation workflows using strong coding practices to enhance efficiency and scalability.
  • Collaboration & Tooling
  • Collaborate with experimental teams, leveraging a hybrid computational + experimental literacy to validate models and drive design iteration.
  • Utilize advanced simulation tools (VASP, Quantum Espresso) and data science libraries (TensorFlow, Pandas) to manage and analyze large datasets.

Education and/or Experience:
  • Education: Ph.D. in Mechanical Engineering, Materials Science, Chemical Engineering, or a closely related computational/physics field.
  • Core Simulation Expertise: Deep and extensive experience in atomistic simulation and quantum modeling, including proficiency with key QM/DFT tools (VASP, Quantum Espresso) and MD simulations.
  • Domain Focus: Strong background in electrochemical energy materials and extensive computational work focused on batteries/fuel cells.
  • Coding Proficiency: Strong coding skills in Python (along with related libraries like Pandas and TensorFlow) for simulation workflow automation and data analysis.
  • ML Application: Experience in developing or utilizing ML-enhanced force fields and surrogate models for materials prediction., or equivalent practical experience.

Preferred Qualifications:
  • LLM Development: Experience in developing battery domain LLM features or property-prediction models.
  • Hybrid Skillset: Demonstrated experience working in a hybrid computational + experimental environment.
  • Tooling Diversity: Familiarity with additional data analysis tools like R, SQL, MATLAB, and time-series forecasting libraries like Prophet.
  • Target Background: Previous experience at national laboratories, XtalPi, Entalpic, or deep battery modeling groups.

The salary range for this position as required under applicable pay transparency laws.
Salary Range
$180,000-$200,000 USD

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