Location: [Lynchburg, VA, USA]
Department: Fuel Design
Job Type: VIE - Full-time

About the Role:

We are seeking a highly motivated AI/Digital Engineer to join our Fuel Design team in transforming how data and machine learning are applied within the nuclear fuel cycle. This role focuses on applying advanced machine learning (ML) and AI techniques to support and enhance decision-making in areas such as fuel cycle optimization, core design, inventory management, and operational forecasting.

You’ll work closely with nuclear engineers, data scientists, and software developers to build, deploy, and maintain AI-powered tools and models that solve complex business and engineering challenges.

Key Responsibilities:

• Propose, develop, and implement AI/ML models to solve real-world problems in nuclear fuel management, including:
  • Fuel loading pattern optimization
  • Burnup and depletion prediction
  • Fuel inventory planning
  • Anomaly detection in reactor operations
• Collaborate with subject matter experts to translate nuclear domain knowledge into model features and constraints.
• Design experiments and simulations using physics-informed machine learning or integrate ML with reactor simulation tools.
• Clean, preprocess, and analyze large datasets (e.g., simulation outputs, operational data).
• Build and maintain custom Gym environments or RL frameworks for nuclear fuel design and optimization.
• Communicate findings through visualizations, dashboards, and technical reports for both technical and non-technical stakeholders.
• Work cross-functionally with engineering, operations, and business units to integrate ML tools into workflows and decision systems.
• Stay current with advancements in AI/ML and evaluate their applicability in the nuclear sector.

Qualifications:

Required:

• B.S. or M.S. in Computer Science, Data Science, Nuclear Engineering, Applied Mathematics, or a related field.
• Demonstrated experience applying automation (using e.g., Python or Bash) on Linux systems to accelerate workflow and enhance data analysis.
• Strong understanding of runtime optimization and parallel computing in a HPC environment.
• Proficiency in Python and ML libraries such as scikit-learn, TensorFlow, PyTorch, or Stable-Baselines3.
• Experience with data handling tools (e.g., NumPy, Pandas, SQL).
  Strong understanding of supervised, unsupervised, and reinforcement learning methods.
• Familiarity with optimization algorithms, constraint handling, and evolutionary computation.
• Ability to explain technical details clearly to non-experts and collaborate across disciplines.

Preferred:

• PhD in Computer Science, Data Science, Nuclear Engineering, Applied Mathematics, or a related field.
• Knowledge of regulatory or economic constraints in nuclear fuel supply chains.