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Focused Energy is building commercial-scale fusion power through laser-driven inertial fusion, backed by a $240M Series A. This role sits at the intersection of ML engineering and computational physics within the Software Engineering & Digital Twin environments.
You will design, build, and deploy machine learning models that enhance and accelerate multiphysics simulation environments across high-energy laser systems, target injection and tracking, target manufacturing, and fusion chambers. Your work will enable faster design cycles, predictive system optimization, and real-time decision support across laser, targetry, and fusion programs.
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) that embed physical constraints directly into model architectures
- Build and maintain ML pipelines for training, validation, uncertainty quantification, and continuous model refinement
- Implement active learning and Bayesian optimization workflows to guide design space exploration
- Integrate trained ML models into the Digital Twin framework, interfacing with HPC simulation outputs and real-time sensor data
- Develop anomaly detection and predictive diagnostics models for system health monitoring
- Apply reinforcement learning and Bayesian control approaches to support autonomous optimization
- Collaborate with Digital Twin architects, systems engineers, and optical simulation scientists
- Establish best practices for model versioning, reproducibility, testing, and documentation
You will work in an interdisciplinary environment spanning physics, engineering, and software, collaborating closely with optical engineers, laser and fusion physicists, simulation scientists, and systems engineers.
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 frameworks (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 with PDE-based simulation outputs, time-series sensor data, and high-dimensional parameter spaces
- Hands-on experience with Gaussian processes, neural network surrogates, reduced-order models, or equivalent metamodeling techniques
- Experience building robust ML pipelines for scientific data (preprocessing, feature engineering, validation, deployment)
- Ability to communicate model behavior, confidence intervals, and limitations to physicists and engineers
- Strong cross-functional team skills and comfort in interdisciplinary environments
Nice-to-haves:
- Experience with physics-informed neural networks (PINNs) or neural operators (DeepONet, FNO)
- Background in laser physics, plasma physics, or high-energy-density science
- Experience with digital twin platforms and live ML integration (NVIDIA Omniverse, Siemens Xcelerator, ANSYS Twin Builder)
- Familiarity with multidisciplinary design optimization (MDO), Design of Experiments, sensitivity analysis
- Experience applying reinforcement learning to physical system control
- Familiarity with Monte Carlo methods and statistical uncertainty quantification
- Experience with MLOps tooling (MLflow, Weights & Biases, DVC) in scientific computing
- Interest in fusion energy or advanced laser systems
