WHAT YOU'LL DO
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
WHO YOU ARE
MUST-HAVES
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
WHAT WE OFFER
Innovative Technology & Mission: Be part of a cutting-edge company working on groundbreaking research in clean energy and scientific discovery.
Career Development: We invest in our people. This is an opportunity to join an early, growing company at the forefront of new technology, where you’ll have the opportunity to develop and grow your career.
Collaborative Culture: Work with great people in a dynamic, international environment with top-tier scientists, engineers and professionals passionate about solving global challenges.
Ownership: Take ownership from day 1 and drive impact by making decisions, solving challenges and shaping the future of our company.
Your Success is our Success: We offer a competitive salary and company ownership in the form of stock options, giving you a direct stake in our success and ownership in what we build together.
Medical, Dental, Vision: Multiple great plans to pick from that are active come the 1st Day of employment for you and any enrolled dependents.
Vacation Days: We support your work-life-balance by granting you Unlimited PTO.
Snacks and Drinks: Our office fridges are never empty. Enjoy our healthy snacks and drinks.
Retirement Plan: If you decide to enroll in our 401k plan, we will match up to 4% on top of the employee contribution.
Equipment: We offer state-of-the-art Windows or Apple Laptops. In addition, you will receive a keyboard, mouse and headset so you are fully equipped from day 1.
Events: We organize regular events to foster team bonding, encourage collaboration, and build a strong, supportive company culture. Connect with colleagues across departments, share ideas, and have fun together, both in and outside of the workplace.
Focused Energy is an equal opportunity employer committed to creating an inclusive environment. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, or disability status.
Pursuant to the San Francisco Fair Chance Ordinance, Focused Energy will consider for employment qualified applicants with arrest and conviction records.
Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, benefits.
