Machine Learning Engineer

Focused Energy Inc.

. Design and deploy surrogate and reduced-order models that replace or accelerate high-fidelity multiphysics simulations .

Posted 9/21/2026full-timeAustin • Texas • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing and deploying machine learning models for complex physical systems, with a strong focus on deep learning frameworks such as PyTorch and TensorFlow. Proficient in integrating ML models into Digital Twin frameworks and optimizing physical system parameters through advanced techniques like reinforcement learning and Bayesian optimization.

Highest-signal resume keywords

Machine Learning Model DevelopmentDeep Learning (PyTorch, TensorFlow)High-Performance Computing (HPC)Digital Twin IntegrationReinforcement Learning

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Machine LearningDeep LearningPythonC++FortranGaussian ProcessesNeural Network SurrogatesModel ValidationUncertainty QuantificationBayesian Optimization

Soft Skills

Cross-Functional Team CollaborationClear Communication

Tools & Technologies

MLflowWeights & BiasesDVCMPIOpenMP

Industry Keywords

Computational PhysicsApplied MathematicsData ScienceLaser PhysicsPlasma PhysicsHigh-Energy-Density ScienceDigital TwinMDO WorkflowsDoEMonte Carlo Methods

Tech Stack

Tools & technologies

PythonPyTorchTensorflowC++

About the role

Key responsibilities & impact

Design and deploy surrogate and reduced-order models that replace or accelerate high-fidelity multiphysics simulations
Develop physics-informed machine learning and physics-informed neural networks embedding physical constraints into model architectures
Build and maintain ML pipelines for training, validation, uncertainty quantification, and continuous refinement against 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 Digital Twin framework, interfacing with HPC simulation outputs and real-time sensor data
Develop anomaly detection and predictive diagnostics models for laser subsystem health monitoring
Apply reinforcement learning and Bayesian control approaches to optimize laser operating parameters
Collaborate with Digital Twin architects, systems engineers, optical simulation scientists, and other interdisciplinary teams
Establish best practices for model versioning, reproducibility, testing, and documentation
Support faster design cycles, predictive system optimization, and real-time decision support across laser, targetry, and fusion programs

Requirements

What you’ll need

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, including PyTorch, TensorFlow/JAX, probabilistic models, and uncertainty quantification
Expert-level Python
Proficiency in C++ or Fortran is a plus
Experience with HPC environments, batch schedulers, and MPI/OpenMP parallelization
Fluency working 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, 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 and comfort working in an interdisciplinary environment
Experience with PINNs or neural operators is a plus
Background in laser physics, plasma physics, high-energy-density science, or related domains is a plus
Experience with digital twin platforms and live ML model integration is a plus
Familiarity with MDO workflows, DoE, sensitivity analysis, and uncertainty propagation is a plus
Experience applying reinforcement learning to physical system control or optimization is a plus
Familiarity with Monte Carlo methods and statistical uncertainty quantification frameworks is a plus
Experience with MLOps tooling such as MLflow, Weights & Biases, or DVC is a plus
Interest in fusion energy, advanced laser systems, or high-energy-density physics is a plus
Eligibility to work in the United States or Germany is addressed in the application form

Benefits

Comp & perks

Competitive salary
Company ownership in the form of stock options
Medical, dental, and vision plans active from the first day of employment for employees and enrolled dependents
Unlimited PTO
401(k) plan with up to 4% employer matching
State-of-the-art Windows or Apple laptop
Keyboard, mouse, and headset
Regular company events
Career development opportunities
Collaborative international work environment