Machine Learning Engineer

Machine Learning Engineer role focused on combining ML engineering with computational physics to deliver data-driven intelligence inside Digital Twin systems for clean energy and fusion programs.

Responsibilities

• Design and deploy surrogate models and reduced-order models (ROMs) to replace or accelerate high-fidelity multiphysics simulations within the Digital Twin environment
• Develop physics-informed machine learning (PIML) and physics-informed neural networks (PINNs) with hard physical constraints integrated into model architectures (Maxwell's equations, thermodynamics, fluid dynamics)
• Build and maintain ML pipelines for training, validation, uncertainty quantification (UQ), and continuous refinement using experimental and simulation data
• Implement active learning and Bayesian optimization workflows to 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 meet fidelity, latency, and uncertainty requirements
• Establish best practices for model versioning, reproducibility, testing, and documentation in a fast-moving research environment

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 deep learning skills (PyTorch, TensorFlow/JAX), probabilistic modeling, and uncertainty quantification
• Expert-level Python; C++ or Fortran proficiency is a plus; experience with HPC environments, including batch schedulers and MPI/OpenMP parallelization
• Comfort working with PDE-based simulation outputs, time-series sensor data, and high-dimensional parameter spaces common in multiphysics
• 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 explain model behavior, confidence intervals, and limitations clearly to physicists, engineers, and non-ML stakeholders
• Strong cross-functional collaboration skills across physics, engineering, and software

Technologies

• PyTorch
• TensorFlow
• JAX
• C++
• Fortran
• COMSOL
• ANSYS
• MPI
• OpenMP
• Gaussian processes
• NVIDIA Omniverse
• Siemens Xcelerator
• ANSYS Twin Builder
• DeepONet
• FNO
• MLflow
• Weights & Biases
• DVC

Benefits

• Competitive salary and company ownership via stock options
• Medical, Dental, Vision with multiple plans active on the 1st Day of employment
• Unlimited PTO
• Healthy snacks and drinks
• 401k plan with match up to 4% on top of the employee contribution
• State-of-the-art Windows or Apple laptops plus additional equipment (keyboard, mouse, headset)
• Regular events to support team bonding and collaboration

Nice-to-haves

• Experience with physics-informed neural networks (PINNs) or neural operators (DeepONet, FNO) for 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 (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 applying MLOps tooling (MLflow, Weights & Biases, DVC) in a scientific computing context
• Interest in fusion energy, advanced laser systems, or high-energy-density physics

What we offer

• Innovative technology and mission: work on clean energy and scientific discovery
• Career development: join an early, growing company focused on new technology
• Collaborative culture: dynamic, international environment with top-tier scientists and engineers
• Ownership: drive impact starting day one
• Your success is our success: competitive compensation plus stock options
• Medical, Dental, Vision: multiple plans active on the 1st Day of employment
• Vacation days: unlimited PTO
• Snacks and drinks: healthy snacks and drinks available in the office
• Retirement plan: 401k match up to 4% on top of employee contribution
• Equipment: state-of-the-art Windows or Apple laptops plus key peripherals from day one
• Events: regular events focused on bonding, collaboration, and culture

Location: Austin, TX (onsite)

Education: Master’s or PhD