Machine Learning Engineer (f/m/d)

About the role

Focused Energy is building the future of clean energy through laser-driven inertial fusion. Backed by our $240M Series A — the largest fully secured Series A in the global fusion industry — we are scaling rapidly across multiple geographies.

Based in Germany and the US, we have brought together the top scientific and engineering minds using proven technologies to unlock fusion power at commercial scale. Focused Energy is working to realize game-changing scientific innovation that promises to deliver a clean, sustainable and abundant source of power globally.

YOUR ROLE

This role sits at the intersection of ML engineering and computational physics, working within our Software Engineering & Digital Twin environments. You'll collaborate closely with optical engineers, laser and fusion physicists, simulation scientists, and systems engineers to embed data-driven intelligence directly into our Digital Twin architecture. You’ll be reducing design risk, shortening development timelines, and unlocking system-level optimization that purely physics-based approaches can't achieve at the speed we need.

You will design, build, and deploy machine learning models that enhance, accelerate, and augment our multiphysics simulation environments across our high-energy laser systems, target injection and tracking system, target manufacturing system, and fusion chamber. This will enable faster design cycles, predictive system optimization, and real-time decision support across our laser, targetry, and fusion programs.

WHAT YOU'LL DO

- Surrogate & reduced-order modeling: Design and deploy surrogate and reduced-order models (ROMs) that replace or accelerate high-fidelity multiphysics simulations in the Digital Twin environment

- Physics-informed ML: Develop physics-informed machine learning (PIML) and physics-informed neural networks (PINNs) that embed physical constraints (Maxwell's equations, thermodynamics, fluid dynamics) directly into model architectures

- ML pipelines: Build and maintain ML pipelines for training, validation, uncertainty quantification (UQ), and continuous model refinement against experimental and simulation data

- Active learning & Bayesian optimization: Implement active learning and Bayesian optimization workflows to intelligently guide design space exploration and reduce costly simulation runs

- Digital Twin integration: Integrate trained ML models into the broader Digital Twin framework, interfacing with HPC simulation outputs (COMSOL, ANSYS, custom solvers) and real-time sensor data

- Anomaly detection: Develop anomaly detection and predictive diagnostics models to monitor system health and identify off-nominal behavior in laser subsystems

- Autonomous optimization: Apply reinforcement learning and Bayesian control approaches to support autonomous or semi-autonomous optimization of laser operating parameters

- Cross-functional collaboration: Collaborate with Digital Twin architects, systems engineers, and optical simulation scientists to ensure ML models meet fidelity, latency, and uncertainty requirements