Thea Energy is developing a stellarator fusion system built on arrays of manufacturable planar magnets, with its Eos device at the center of current work. The stack pairs scientific computing (Fortran, Julia, HPC, COMSOL, ANSYS) with power-systems and controls tooling (PSCAD, ETAP, SCADA, LabVIEW), and it is adopting SCADA and FPGA. Hiring is accelerating and heavily engineering-weighted with many senior and director roles, which points to a shift from physics design toward building and controlling hardware.
Thea Energy is a privately held fusion energy company founded in 2022 as a spinout of Princeton University and Princeton Plasma Physics Laboratory. It is commercializing the stellarator, a magnetic confinement fusion approach, using planar coil magnets and software-driven controls. The company is headquartered in Kearny, New Jersey, employs 51–200 people, and hires in the United States. It has received U.S. Department of Energy program support, including Milestone-Based Fusion Development, INFUSE and ARPA-E SCALEUP awards. It works with national labs, academic institutions and industrial partners.
Thea Energy uses Python, Fortran, C++, Julia, MATLAB and HPC for simulation, ANSYS and COMSOL for analysis, SolidWorks, CATIA and Siemens NX for CAD, plus SCADA, LabVIEW and Altium Designer. It is adopting FPGA.
Current work centers on the Eos planar coil stellarator, covering magnet design, build and test, a diagnostics roadmap, control-system data acquisition, assembly and integration planning, and a divertor program.
Yes. Hiring is accelerating and dominated by engineering, with senior and director-level roles prominent, plus smaller research and manufacturing needs, all based in the United States.
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Thea Energy's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.