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Focused Energy is building commercial-scale laser-driven inertial fusion technology, backed by a $240M Series A—the largest fully secured Series A in the global fusion industry. The company operates across Germany and the US, combining proven technologies with top scientific and engineering talent to unlock fusion power at commercial scale.
You will serve as a Machine Learning Engineer at the intersection of ML engineering and computational physics, embedded within the Software Engineering & Digital Twin environments. Working closely with optical engineers, laser and fusion physicists, simulation scientists, and systems engineers, you will design, build, and deploy machine learning models that enhance multiphysics simulation environments across high-energy laser systems, target injection and tracking, target manufacturing, and fusion chambers.
Key responsibilities include:
• Design and deploy surrogate and reduced-order models (ROMs) that replace or accelerate high-fidelity multiphysics simulations in the Digital Twin
• Develop physics-informed machine learning (PIML) and physics-informed neural networks (PINNs) embedding physical constraints (Maxwell's equations, thermodynamics, fluid dynamics) into model architectures
• Build and maintain ML pipelines for training, validation, uncertainty quantification, and continuous model refinement against experimental and simulation data
• Implement active learning and Bayesian optimization workflows to intelligently guide design space exploration and reduce costly simulation runs
• Integrate trained ML models into the broader Digital Twin framework, interfacing with HPC simulation outputs (COMSOL, ANSYS, custom solvers) and real-time sensor data
• Develop anomaly detection and predictive diagnostics models to monitor system health and identify off-nominal behavior in laser subsystems
• Apply reinforcement learning and Bayesian control approaches to support autonomous or semi-autonomous optimization of laser operating parameters
• Collaborate with Digital Twin architects, systems engineers, and optical simulation scientists to ensure ML models meet fidelity, latency, and uncertainty requirements
• Establish best practices for model versioning, reproducibility, testing, and documentation in a fast-moving research environment
REQUIREMENTS:
Must-haves:
• Master's or PhD in Machine Learning, Computational Physics, Applied Mathematics, Data Science, Computer Science, or closely related field
• Proven experience building, training, and deploying ML models for complex physical systems; strong command of deep learning (PyTorch, TensorFlow/JAX), probabilistic models, and uncertainty quantification
• Expert-level Python; proficiency in C++ or Fortran a plus; experience with HPC environments (batch schedulers, MPI/OpenMP parallelization)
• Fluency working with PDE-based simulation outputs, time-series sensor data, and high-dimensional parameter spaces typical of multiphysics environments
• Hands-on experience with Gaussian processes, neural network surrogates, reduced-order models, or equivalent metamodeling techniques
• Experience building robust ML pipelines for scientific data—including preprocessing, feature engineering, model validation, and deployment
• Ability to communicate model behavior, confidence intervals, and limitations clearly to physicists, engineers, and non-ML specialists
• Strong cross-functional team skills; comfort working in an interdisciplinary environment spanning physics, engineering, and software
Nice-to-haves:
• Experience with physics-informed neural networks (PINNs) or neural operators (DeepONet, FNO) applied to physical systems
• Background in laser physics, plasma physics, high-energy-density science, or related complex physics domains
• Experience with digital twin platforms and live integration of ML models with simulation environments (e.g., NVIDIA Omniverse, Siemens Xcelerator, ANSYS Twin Builder)
• Familiarity with multidisciplinary design optimization (MDO) workflows, including Design of Experiments (DoE), sensitivity analysis, and uncertainty propagation
• Experience applying reinforcement learning to physical system control or optimization
• Familiarity with Monte Carlo methods and statistical uncertainty quantification frameworks
• Experience with MLOps tooling (MLflow, Weights & Biases, DVC) in a scientific computing context
• Interest in fusion energy, advanced laser systems, or high-energy-density physics
