Machine Learning Engineer

Project Description :

We are seeking a skilled Machine Learning Engineer to develop and deploy Graph Neural Network (GNN) based surrogate models that approximate complex physics simulations for oil & gas pipeline and well networks. This is a hands-on role for someone who can build high-fidelity neural network models that replace computationally expensive reservoir and network simulators (Nexus, Prosper).

Responsibilities :

• Design and implement Neural Network architectures to model flow dynamics in interconnected pipeline networks
• Build surrogate models that accurately predict pressure distributions, flow rates, and network behavior under varying operational scenarios (training data is acquired through running simulations of the physics models)
• Create data pipelines to extract network topology and simulation results from physics-based models (Nexus / Prosper) and transform them into graph representations
• Develop training frameworks that incorporate physics constraints (conservation laws, pressure-flow relationships) into neural network loss functions
• Collaborate with petroleum engineers to ensure model predictions align with physical behavior and operational constraints
• Implement model monitoring, validation, and continuous improvement workflows
• business trip to Kuwait

Mandatory Skills Description :

Technical Domain Knowledge :

Nice-to-Have Skills Description :

Industry Experience :

Educational Background :