Staff Control Systems Engineer
Thea Energy
- Location
- Kearny, NJ
- Work style
- On-site
- Job type
- Full time
- Posted
- Nov 4
- 1 day ago
Career path
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About the company
Thea Energy
Renewables & environment · 11 to 50 employees
Thea Energy is leveraging recent breakthroughs in stellarator physics and engineering to create a faster and simpler approach to commercializing fusion energy. The company is reinventing the stellarator using computer-controlled arrays of planar coils thereby replacing the intricate, complex modular magnets required in all other stellarator architectures. Thea Energy is on a mission to create a limitless source of zero emission energy for a sustainable future.
What you'll do
- Design, develop, integrate, and test control, instrumentation, and data acquisition systems
- Translate plant and physics requirements into implementable, testable solutions
- Collaborate with subsystem owners, engineers, vendors, and research partners to resolve technical interfaces
- Contribute from requirements and architecture through implementation, integration, verification, and commissioning readiness
What you need
- Bachelor's or Master's degree in electrical engineering, control systems, computer engineering, physics, or a related technical discipline, or an equivalent combination of relevant education and experience.
- 10 or more years of experience developing integrated control, automation, embedded, instrumentation, or data acquisition systems for complex, high-reliability environments.
- Demonstrated experience making substantial technical contributions to multidisciplinary engineering work, reviewing designs, mentoring engineers, and collaborating across disciplines or locations.
- Demonstrated ability to architect and deliver systems that combine industrial controls, supervisory control, real-time software, and hardware interfaces.
- Hands-on experience with EPICS or a comparable distributed-control framework, SCADA/HMI systems, and PLC-based automation.
- Experience defining machine protection, interlock, fail-safe, or emergency-shutdown logic for valuable or safety-significant equipment.
- Proficiency in C or C++, Python, and Linux or real-time operating environments used for control and data acquisition.
- Working knowledge of industrial and network protocols such as OPC UA, Modbus RTU/TCP, and TCP/IP.
- Experience integrating instrumentation or high-speed data acquisition, including at least one FPGA, PXI, CompactRIO, LabVIEW, or comparable hardware-software platform.
- Ability to produce clear specifications, interface control documents, architecture diagrams, test plans, operating procedures, and design-review evidence.
- Hands-on experience with integration, acceptance testing, troubleshooting, and commissioning-readiness activities involving internal teams and external suppliers.
- Strong written and verbal communication skills and sound technical judgment in multidisciplinary engineering environments.
- Experience with nuclear fusion, particle accelerators, large scientific facilities, defense systems, advanced energy systems, or other first-of-a-kind high-reliability programs.
- Experience delivering control systems for ITER, DIII-D, JT60-SA, or a comparable large-scale scientific collaboration.
- Knowledge of IEC 61508, IEC 61511, nuclear quality practices, or other safety-related control-system standards and workflows.
- Experience with Siemens, Beckhoff, Allen-Bradley, or comparable PLC platforms and with CS-Studio or Phoebus operator interfaces.
- Hands-on experience with real-time embedded Linux, RTOS-based systems, deterministic control loops, digital signal processing, or FPGA development.
- Familiarity with distributed timing and communication technologies such as DDS, PTP, White Rabbit, or comparable deterministic networking approaches.
- Experience with MATLAB, Simulink, plant or subsystem dynamic modeling, or model-based control design.
- Experience using Git, Docker, Jenkins, GitLab CI, or comparable configuration-management and continuous-delivery tools in an engineering environment.
- Exposure to predictive control, digital twins, machine learning, or data-driven diagnostics for complex plant systems.
Key skills
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