- 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
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.