Computational Materials Scientist

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.