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Computational Materials Science Engineer Jobs (NOW HIRING)

Entegris, Inc.

$67K - $92K/yr

D. in Chemistry, Materials Science, Physics, Engineering, or related scientific discipline.* Strong foundation in computational materials science, computational chemistry, molecular modeling ...

SES

Computational Materials Scientist

Woburn, MA · On-site +1

$180K - $200K/yr

This powerful combination of "AI for science" and material engineering enables batteries that can ... As a Computational Materials Scientist, you will be a core data-driven modeler responsible for ...

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How much do computational materials science engineer jobs pay per year?

As of Sep 10, 2026, the average yearly pay for computational materials science engineer in the United States is $90,511.00, according to ZipRecruiter salary data. Most workers in this role earn between $68,500.00 and $105,000.00 per year, depending on experience, location, and employer.

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Infographic showing various Computational Materials Science Engineer job openings in the United States as of August 2026, with employment types broken down into 83% Full Time, 13% Part Time, 1% Temporary, 2% Contract, and 1% Nights. Highlights an 92% Physical, 2% Hybrid, and 6% Remote job distribution, with an average salary of $90,511 per year, or $43.5 per hour.

Computational Materials Scientist

Santa Fe, NM • On-site

Discovery 2 Scale
See more jobs at Discovery 2 Scaleexternal

Other

Medical, Retirement

Posted 23 days ago

Job description

Discovery 2 Scale is building the Autonomous Foundry for Advanced Materials—an end-to-end platform connecting materials design, autonomous experimentation, qualification, and scale-up to deliver production-ready materials faster and with less risk.

We are looking for a highly skilled computational materials scientist with strong CALPHAD and Thermo-Calc experience to help build the thermodynamic intelligence behind our platform.

What you’ll do
  • Develop CALPHAD and Thermo-Calc workflows for alloy design and high-throughput screening.
  • Model phase stability, solidification, segregation, and phase transformations.
  • Perform Scheil and kinetic simulations to evaluate manufacturability and processing windows.
  • Develop models for weldability, heat-affected-zone behavior, and thick-section producibility.
  • Build automated Python and Thermo-Calc pipelines for large composition and processing spaces.
  • Integrate thermodynamic predictions with AI/ML models and experimental data to guide alloy design.
Required qualifications
  • M.S. or Ph.D. in Materials Science, Metallurgy, Computational Materials Science, or a related field.
  • Hands-on experience with Thermo-Calc and CALPHAD.
  • Experience modeling multicomponent metallic alloy systems.
  • Experience with equilibrium phase and Scheil solidification calculations.
  • Strong understanding of physical metallurgy, thermodynamics, solidification, and phase transformations.
  • Experience with scientific programming or workflow automation using Python or similar tools.
  • Ability to connect computational predictions with real materials processing and experimental observations, and work independently in a fast-paced startup environment.
Preferred qualifications
  • Experience with TC-Python, DICTRA, TC-PRISMA, or related thermodynamic and kinetic modeling tools.
  • Experience with steels, nickel-based alloys, titanium or refractory alloys, or multi-principal-element alloys.
  • Experience with welding metallurgy, HAZ transformations, solidification, segregation, or casting.
  • Experience integrating CALPHAD predictions with SEM, EDS, EBSD, or other experimental data.
  • Experience combining CALPHAD with machine learning, materials informatics, or high-throughput alloy design.
Why join D2S?
  • Build cutting-edge autonomous materials discovery technology.
  • Work alongside world-class scientists and engineers.
  • High level of ownership and technical freedom.
  • Opportunity to help build a company from the ground up.
  • Competitive salary, comprehensive health benefits, and a 401(k) with company match.
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