Machine Learning Engineer

Focused•Austin, TX

About The Position

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. 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.

Requirements

Master's or PhD in Machine Learning, Computational Physics, Applied Mathematics, Data Science, Computer Science, or a 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

Responsibilities

Design and deploy surrogate and reduced-order models (ROMs) that replace or accelerate high-fidelity multiphysics simulations in the Digital Twin environment
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
Build and maintain ML pipelines for training, validation, uncertainty quantification (UQ), 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