Senior Plasma Control Engineer

About Thea Energy

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.

Thea Energy is seeking a Sr. Plasma Control Engineer to develop and deploy novel plasma control algorithms for the Eos stellarator, with a strong emphasis on AI and ML assisted control, real-time state estimation, and digital twins. This role sits in the Electrical Engineering and Controls Systems (EECS) team and works day to day with plasma physics, diagnostics, and plant control engineers to bridge plasma physics objectives with plasma control and plant control implementation.

If you can develop plasma control algorithms that are robust, constraint-aware, and ready for experimental operation, covering scenario design, supervisory control, and closed-loop regulation of core profiles, edge and divertor regimes, and off-normal response, we would like to hear from you. You will also contribute to subsystem digital twins and data pipelines that enable model development, validation, and deployment.

• Plasma control algorithms and real-time software
  • Develop, test, and iterate plasma control algorithms for Eos PCS, including multi-rate feedback, supervisory logic, and constraint handling.
  • Build AI/ML assisted control modules, such as learning-augmented MPC, reinforcement learning in simulation, physics-based models augmented with data-driven corrections, and adaptive control and auto-tuning across operating conditions.
  • Develop and deploy real-time state estimation and multi-diagnostic data fusion to produce control-grade plasma state, including robust profile reconstruction and regime indicators suitable for closed-loop operation.
  • Develop anomaly detection and early-warning indicators across plasma and plant signals, with clear thresholds and graded mitigation actions.
  • Partner with plasma physicists to translate physics goals into control objectives, observables, constraints, and validation tests.
• Digital twins and modeling
  • Design and maintain digital twin models for plasma control loops and coupled plant subsystems, enabling simulation, optimization, and controller development and validation.
  • Build model validation workflows, including post-shot reconstruction, scenario sweeps, sensitivity studies, and uncertainty-aware comparisons against experimental data.
• Data and integration
  • Build and maintain data pipelines for time-series control and diagnostic data, including labeling, archiving, and training and inference workflows.
  • Integrate algorithms into the control stack in collaboration with EECS, ensuring compatibility with real-time constraints and low-latency inference where needed.
  • Maintain high software quality: version control, test coverage, reproducible environments, and operational readiness documentation.

Required qualifications:

• BS or MS in Electrical Engineering, Computer Science, Physics, Applied Mathematics, or related field.
• 3 or more years of experience developing control algorithms, ML models, or real-time scientific software for complex physical systems.
• Strong programming skills in Python, MATLAB and C++.
• Experience with time-series data processing, signal conditioning, system identification, and validation on real data.
• Practical familiarity with modern ML tooling (PyTorch, Tensor Flow, or JAX) and deploying models into production code paths.
• Working knowledge of modern control methods, such as state estimation, constrained optimization, MPC, and robust control.

Preferred qualifications:

• Experience with plasma control, stellarators or tokamaks, or adjacent fields such as accelerators and large scientific infrastructure.
• Prior work on digital twins, real-time simulators, or physics-informed ML for physical systems.
• Experience integrating with experimental control…