Machine Learning Engineer (m/f/d)

About This Opportunity

We are representing a confidential fusion energy organization at the forefront of clean energy innovation. Our client is building next-generation laser-driven systems and requires a Machine Learning Engineer to accelerate their computational physics and simulation capabilities.

This role sits at the intersection of ML engineering and multiphysics modeling. You'll work within digital twin environments, collaborating with optical engineers, physicists, simulation scientists, and systems engineers to embed data-driven intelligence into complex hardware and software architectures. Your work will reduce design risk, shorten development cycles, and enable system-level optimization that traditional physics-based approaches cannot achieve at the required speed.

Location: Darmstadt, Germany

Responsibilities

• Design and deploy surrogate and reduced-order models (ROMs) that replace or accelerate high-fidelity multiphysics simulations in digital twin environments
• Develop physics-informed machine learning (PIML) and physics-informed neural networks (PINNs) that embed physical constraints directly into model architectures, including Maxwell's equations, thermodynamics, and fluid dynamics
• 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 broader digital twin frameworks, interfacing with HPC simulation outputs and real-time sensor data
• Develop anomaly detection and predictive diagnostics models to monitor system health and identify off-nominal behavior
• Apply reinforcement learning and Bayesian control approaches to support autonomous or semi-autonomous optimization of operating parameters
• Collaborate with digital twin architects, systems engineers, and 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

What We're Looking For

Required Qualifications:

• 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 frameworks including PyTorch, TensorFlow, or JAX
• Expert-level Python programming; proficiency in C++ or Fortran is a plus
• Experience with HPC environments including batch schedulers and 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 collaboration skills; comfort working in interdisciplinary environments spanning physics, engineering, and software

Preferred Qualifications:

• Experience with physics-informed neural networks (PINNs) or neural operators such as DeepONet or 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
• Familiarity with multidisciplinary design optimization (MDO) workflows including Design of Experiments, 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 such as MLflow, Weights & Biases, or DVC in scientific computing contexts
• Interest in fusion energy, advanced laser systems, or high-energy-density physics

How We Work With You

Candidates apply directly through Get A Job.ai. Our talent team will conduct an initial screening to understand your background and technical expertise. Once we identify a strong match, we submit your profile to our client for consideration. Please do not attempt to contact the client directly, as all communication is managed through our recruiting process to ensure confidentiality and a streamlined experience.

Pay

Compensation details will be discussed during the screening process based on your experience level and qualifications.

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