ML Engineer for Scientific Digital Twin & Fusion

Focused Energy LLC; Austin, Northern (TX, KY); Hybrid; USD 140,000 - 200,000; Full time

Benefits offered by this job

Stock options

Competitive salary

Medical, dental, vision insurance

Unlimited PTO

401k with company match

Equipment provided

Team events

Job summary

Focused Energy is pushing the boundaries of clean energy with laser-driven fusion. We seek an ML engineer to design, train, and deploy models that accelerate multiphysics simulations within our Digital Twin ecosystem.

You will work with optical engineers, fusion physicists, and simulation scientists to embed data-driven intelligence across laser systems, targetry, and chamber operations. You will build surrogate models, physics-informed ML, and robust ML pipelines, enabling real-time decision

Qualifications

• Master's or PhD in ML, Computational Physics, Applied Mathematics, Data Science, Computer Science, or a closely related field
• 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

Responsibilities

• 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
• Engineering rigor: Establish best practices for model versioning, reproducibility, testing, and documentation in a fast-moving research environment

Skills

ML engineering

Computational physics

Python

C++

HPC environments

Uncertainty quantification

PDE-based data

Communication

Education

Master's or PhD in ML/Computational Physics/Applied Mathematics/Data Science/CS

Tools

PyTorch

TensorFlow/JAX

MPI/OpenMP

COMSOL

ANSYS

Job description

Focused Energy is pushing the boundaries of clean energy with laser-driven fusion. We seek an ML engineer to design, train, and deploy models that accelerate multiphysics simulations within our Digital Twin ecosystem.

You will work with optical engineers, fusion physicists, and simulation scientists to embed data-driven intelligence across laser systems, targetry, and chamber operations. You will build surrogate models, physics-informed ML, and robust ML pipelines, enabling real-time decision