Senior Plasma Control Engineer

Thea Energy•Kearny, NJ

1d•Onsite

About The Position

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. The ideal candidate 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. The role also involves contributing to subsystem digital twins and data pipelines that enable model development, validation, and deployment.

Requirements

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, TensorFlow, 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.
Ability to occasionally lift up to 50 lbs.
Ability to perform activities such as typing, standing, or sitting for extended periods of time.
Willingness to occasionally travel or work required nights/weekends/on-call.
Ability to work in a facility that contains industrial hazards including heat, cold, noise, fumes, strong magnets, high voltage, high current, pressure systems, and cryogenics.

Nice To Haves

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 and data systems (for example EPICS, MDSplus, SCADA, OPC UA).
Familiarity with low-latency deployment and inference engines such as ONNX Runtime and TensorRT.
Background in any of the following plant subsystems is a plus: power electronics, cryogenics, vacuum and pumping, high-power RF systems, and safety controls.

Responsibilities

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.
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.
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.