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Commonwealth Fusion Systems is seeking a Staff Computational Materials Scientist to join the Materials and Processing Department in support of SPARC, a net-energy tokamak under construction in Devens, MA, and the ARC fusion power plant design. The role focuses on computational modeling and materials science to address the extreme-environment challenges of fusion energy systems, including neutron irradiation, high heat loads, molten salt coolant compatibility, cryogenic mechanical loading, and high magnetic fields.
You will develop and deploy computational toolsets and workflows to predict material properties under extreme conditions with quantified uncertainty bounds. Key responsibilities include: building physics-informed models and synthetic data generation using first-principles simulation; identifying chemical and microstructural features that drive material performance; designing experiments for materials subjected to simultaneous corrosion, stress, irradiation, and extreme thermal/magnetic/electrical conditions; developing material design curves and statistical methodologies for tokamak applications (fatigue, dielectric breakdown, radiation effects); identifying knowledge gaps addressable through simulation; benchmarking material property variability across manufacturing and assembly processes; collaborating on data capture, storage, visualization, and analysis workflows; and supporting materials standards documentation.
You will work cross-functionally with design engineers, analysis teams, and other materials specialists to prioritize experimental work, quantify process-structure-properties relationships, and translate computational insights into engineering decisions. The role demands both deep technical expertise in computational materials science and the ability to communicate complex quantitative findings clearly to diverse technical audiences.
REQUIREMENTS:
- Education: Materials Science and Engineering, Applied Physics, Applied Mathematics, Physical Chemistry, Computer Science, or related field
- Minimum 10+ years of experience in experimental or computational materials science
- Proficiency with computational materials science tools and integrated computational materials engineering (ICME) frameworks
- Implementation and/or authorship of codes for material property and microstructure calculations and analyses
- Demonstrated experience building predictive models for material properties and microstructure evolution in extreme environments
- Model validation against experimental data or physical mechanism bounds
- Deep understanding of process-structure-properties-performance principles and fundamental mechanisms driving property changes in extreme environments
- Knowledge of materials simulation methods across length scales: atomistic, molecular dynamics, density functional theory, Monte Carlo, phase field, crystal plasticity, dislocation dynamics, computational thermodynamics, and finite element methods
- Strong scientific computing foundation: numerical algorithms, FEM, FD, FVM, BEM, eigen/linear/PDE solvers, convex optimization, computational geometry, advanced statistical methods
- High-performance computing experience: distributed processing, message passing interface, GPU acceleration
- Understanding of uncertainty quantification in model inputs and outputs
- Demonstrated ability to work effectively cross-functionally with strong organizational and prioritization skills
- Ability to decompose complex problems into valuable, manageable deliverables
- Physical capability to perform typing and sitting for extended periods; dedication to safety protocols for industrial hazards (heat, cold, noise, fumes, strong magnets, lead, high voltage, cryogenics)
- Willingness to travel occasionally (less than 10%) and work required nights/weekends/on-call as needed
BONUS: Experience presenting complex quantitative data intuitively; decision-making in fast-paced engineering settings; technical communication with engineering teams; driving under-defined problems to completion; operating in flexible, service-oriented team environments.
