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Computational Materials Science Jobs (NOW HIRING)

Novelis

Lead Modeling Scientist

Kennesaw, GA

D.) in Materials Science, Mechanical Engineering, or a related field, with more than ten years of experience in computational modeling. * Expertise in metallurgy and materials science, as well as ...

Worcester Polytech

Post-Doctoral Fellow

Worcester, MA · On-site

$45K - $70K/yr

... computational materials science with a focus on advanced density functional theory (DFT), machine learning, and multiscale materials modeling for alloys and ceramics. The successful candidate will ...

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Computational Materials Science information

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$142.5K

$168.8K

$192.5K

How much do computational materials science jobs pay per year?

As of Jul 25, 2026, the average yearly pay for computational materials science in the United States is $168,844.00, according to ZipRecruiter salary data. Most workers in this role earn between $155,500.00 and $182,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Computational Materials Scientist, and why are they important?

To thrive as a Computational Materials Scientist, you need a solid background in materials science, physics, or chemistry, often with a graduate degree and experience in scientific computing. Proficiency with simulation software (such as VASP, LAMMPS, or Quantum ESPRESSO), programming languages (like Python, C++, or Fortran), and familiarity with high-performance computing systems is typically required. Critical thinking, problem-solving abilities, and effective collaboration and communication skills set outstanding candidates apart. These competencies are crucial for designing, executing, and interpreting complex simulations and for translating computational insights into real-world materials innovations.

What is the difference between Computational Materials Science vs Materials Engineer?

AspectComputational Materials ScienceMaterials Engineer
Required CredentialsTypically requires a PhD or Master's in materials science, physics, or chemistryBachelor's or Master's in materials engineering or related field
Work EnvironmentResearch labs, universities, or R&D departments focusing on simulations and modelingManufacturing plants, design offices, or product development teams
Industry UsagePrimarily in research, academia, and advanced R&D projectsProduction, quality control, and product development in manufacturing industries
Common Search/ComparisonYesYes

Computational Materials Science focuses on using computer simulations and modeling to understand and predict material behavior, often requiring advanced degrees. Materials Engineers work on designing, testing, and improving materials in practical applications, usually with a bachelor's or master's degree. While both roles are integral to materials development, Computational Materials Science is more research-oriented, whereas Materials Engineering emphasizes application and production.

What are some common challenges faced by professionals in Computational Materials Science, and how can they be addressed?

Professionals in Computational Materials Science often encounter challenges such as dealing with large datasets, managing the complexity of multi-scale simulations, and ensuring the accuracy of computational models. Addressing these challenges typically involves staying updated on the latest simulation software, collaborating closely with experimental teams to validate results, and developing strong programming and data analysis skills. Effective communication and interdisciplinary teamwork are also key, as projects often require input from chemists, physicists, and engineers to achieve successful outcomes.

What Are Computational Materials Science Jobs?

Jobs in computational materials science include academic and research positions in university settings. You can also find positions in the manufacturing industry. As a research scientist in computational materials science, your duties are to develop hypotheses and test them using computational modeling software and a variety of investigatory tools, such as Monte Carlo algorithms, density function theory, phase field models, and finite element methods. Your responsibilities include gathering data, testing modeling software, collaborating with other researchers to develop tools that aid them in their research, and analyzing data to write reports, journal articles, or presentations for conferences.

What is computational materials science?

Computational materials science is a field that uses computer-based simulations and modeling to understand, predict, and design the properties and behaviors of materials. Researchers use mathematical models, algorithms, and high-performance computing to study materials at the atomic, molecular, or macroscopic level. This approach allows scientists to accelerate the discovery of new materials, optimize existing ones, and investigate phenomena that may be difficult or expensive to study experimentally.
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What cities are hiring for Computational Materials Science jobs? Cities with the most Computational Materials Science job openings:
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Computational Materials Science jobs near you
Infographic showing various Computational Materials Science job openings in the United States as of July 2026, with employment types broken down into 67% Full Time, 32% Part Time, and 1% Contract. Highlights an 66% Physical, 2% Hybrid, and 32% Remote job distribution, with an average salary of $168,844 per year, or $81.2 per hour.
Senior Research Scientist

Senior Research Scientist

Toyota Research Institute

Los Altos, CA • On-site

$180K - $258K/yr

Full-time

Posted 16 days ago

Job description

At Toyota Research Institute (TRI), we’re on a mission to improve the quality of human life. We’re developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility, we’ve built a world-class team advancing the state of the art in AI, robotics, driving, and material sciences.


THE CHALLENGE
The long-term vision of TRI’s Accelerated Materials Design and Discovery (AMDD) program is to accelerate the development of truly emissions-free mobility. Realizing this vision will require the discovery of new materials and devices for batteries, fuel cells, and more. Our aim at TRI is to merge sophisticated computational materials modeling, new experimental data, artificial intelligence, and automation to significantly accelerate materials research. Our focus is on developing tools and capability to enable this acceleration. We collaborate closely with colleagues across global Toyota, as well as with universities and national labs. AMDD seeks to develop and translate the newest technologies into practice, both within Toyota and the open research community.
 
THE OPPORTUNITY
 
We are hiring a Senior Research Scientist to help deepen the chemistry at the core of our materials-design models. This is a full-time role with the scope to take problems from framing through to validation. We are looking for researchers with expertise in uncovering the chemicalrelationships that govern complex materials and drive their discovery. 
Our goal is to integrate this fundamental chemical intuition as a primary, first-class input within our models, moving beyond post-hoc analysis. The team is currently developing representations where internal variables are engineered to represent chemical meaning, rather than only statistical correlations. We are ready to expand this vision with a scientist who can further sharpen the underlying chemistry of our approach.

In this role, you will work alongside ML researchers and software engineers to translate these insights into practical tools that drive real-world materials decisions. You will have the opportunity to publish your findings in leading venues across both materials chemistry and the AI-for-materials community. Join us in defining a future where chemical heuristics are central to modern, AI-driven materials discovery.

THE TEAM

You will expand your professional network, as part of a creative team of scientists and engineers dedicated to enabling a sustainable future. Our team prioritizes learning new skills together at the interface of materials science and AI. You will be part of the Energy & Materials division which is accelerating Toyota’s path to carbon neutrality. In addition to our work on accelerating materials discovery, the division develops capabilities for AI assisted manufacturing, AI technologies to scale from materials to devices, and provides strategic analysis on carbon neutral pathways and technologies.

Responsibilities
  • Develop chemically grounded hypotheses that guide materials discovery and help clarify which scientific relationships matter most.
  • Translate fundamental chemical intuition into scientific inputs, constraints, evaluation criteria, and validation strategies for AI-assisted materials workflows.
  • Analyze complex inorganic and functional materials systems to identify structure-property relationships relevant to batteries, fuel cells, and other emissions-free mobility technologies.
  • Partner with ML researchers and software engineers to ensure that materials-design tools reflect sound chemical reasoning and are scientifically meaningful.
  • Design and assess workflows that connect computational materials modeling, experimental data, literature evidence, and AI-assisted prediction or optimization.
  • Evaluate model outputs using domain knowledge, first-principles calculations, experimental data, or other appropriate scientific benchmarks.
  • Communicate scientific insights clearly through internal technical discussions, cross-functional collaborations, publications, and presentations.
  • Collaborate with colleagues across TRI, Toyota, universities, and national labs to translate emerging materials-discovery methods into useful research capabilities.
Qualifications
  • Hold a PhD in solid-state/inorganic chemistry, materials chemistry, materials science, condensed-matter physics, or a related field.
  • Have a strong publication record using chemical reasoning to predict or design functional inorganic materials.                                                 
  • Have hands-on experience with at least one of: solid-state synthesis, structural characterization, electrochemistry, or first-principles workflows.                                                 
  • Are excited to translate fundamental chemical understanding into practicaltools that address real industrial problems.                                    
  • Thrive in a culture that values diversity, collaboration, humility, and learning. 

The pay range for this position at commencement of employment is expected to be between $180,000 and $258,750/year for California-based roles. Base pay offered will depend on multiple individualized factors, including, but not limited to, a candidate's experience, skills, job-related knowledge, and market location. TRI offers a generous benefits package including medical, dental, and vision insurance, 401(k) eligibility, paid time off benefits (including vacation, sick time, and parental leave), and an annual cash bonus structure. Additional details regarding these benefit plans will be provided if an employee receives an offer of employment.


Please reference this Candidate Privacy Notice to inform you of the categories of personal information that we collect from individuals who inquire about and/or apply to work for Toyota Research Institute, Inc. or its subsidiaries, including Toyota A.I. Ventures GP, L.P., and the purposes for which we use such personal information.
 
TRI is fueled by a diverse and inclusive community of people with unique backgrounds, education and life experiences. We are dedicated to fostering an innovative and collaborative environment by living the values that are an essential part of our culture. We believe diversity makes us stronger and are proud to provide Equal Employment Opportunity for all, without regard to an applicant’s race, color, creed, gender, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, medical condition, religion, marital status, genetic information, veteran status, or any other status protected under federal, state or local laws.
 
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. Pursuant to the San Francisco Fair Chance Ordinance, we will consider qualified applicants with arrest and conviction records for employment.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.