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. #J-18808-Ljbffr Discovery 2 Scale