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General information Legal company Framatome is an international leader in nuclear energy recognized for its innovative, digital and value added solutions for the global nuclear fleet. With worldwide expertise and a proven track record for reliability and performance, the company designs, services and installs components, fuel, and instrumentation and control systems for nuclear power plants. Its more than 20 000 employees work every day to help Framatome's customers supply ever cleaner, safer and more economical low-carbon energy.

Visit us at www.framatome.com, and follow us on Twitter: @Framatome_ and LinkedIn: Framatome. Framatome is owned by the EDF Group (80.5%) and Mitsubishi Heavy Industries (MHI - 19.5%). Reference number 2026-25329 Publication date 3/4/2026 Job details Category TA - DESIGN & ENGINEERING - TAH - Safety, process and systems design - Reactor operation & functional design Job Opening Title VIE - Digital Engineer F/H Job type [France] VIE Remuneration annuelle minimum en 44928 Remuneration annuelle maximum en 44928 BU description S'appuyant sur plus de 40 annees de savoir-faire, et les competences de 5 000 collaborateurs a travers le monde, la Business Unit (BU) Fuel developpe, concoit, fabrique et commercialise des assemblages de combustible ainsi que des services associes au combustible, pour les centrales de production d'electricite de type reacteur a eau legere (REP pour les reacteurs a eau sous pression et REB pour les reacteurs a eau bouillante) et les reacteurs de recherche

La BU Fuel est egalement en charge de l'elaboration du zirconium et de ses alliages et commercialise aussi des services d'ingenierie et des services sur site associes au combustible. Job description 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. Profile 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. Job location Job location USA, Virginia, Lynchburg Job location (site) Lynchburg BU (Organization) FL - FD Applicant criteria Minimum level of education required Master Minimum level of experience required Student Employment level Cadre Languages French (Good knowledge) English (Good knowledge) Extra informations Background checking required .

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