Machine Learning Engineer (on-site)

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 behaviour 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 behaviour and operational constraints.
• Implement model monitoring, validation, and continuous improvement workflows.
• Business trip to Kuwait for first 6-12 months. On-site.

Skills

• Must have:
• Strong expertise in Graph Neural Networks (GCN, GraphSAGE, Message Passing Networks) with proven implementation experience.
• Deep understanding of deep learning frameworks (PyTorch Geometric, DGL, or TensorFlow GNN).
• Experience building surrogate models or physics-informed neural networks (PINNs) for engineering applications.
• Proficiency in Python and scientific computing libraries (NumPy, SciPy, Pandas).
• Demonstrated ability to work with complex data structures (graphs, time-series, spatial data).
• Understanding of optimization techniques and handling large-scale training data.
• Understanding of graph theory and network analysis.
• Experience with data structures and graph representations (adjacency matrices, edge lists, sparse tensors).
• Knowledge of hyperparameter tuning, model building and uncertainty quantification in ML models.
• Ready for a long term business trip to Kuwait for first 6-12 months.
• Nice to have:
• Background in petroleum engineering, process engineering, or fluid dynamics.
• Familiarity with reservoir simulation or pipeline hydraulics.
• Experience with MLOps practices and model lifecycle management.
• Publications or open-source contributions in graph ML.
• Experience deploying ML models in production cloud environments (containerization, API development).
• Industry experience in Oil & Gas is a strong plus; however, candidates with relevant surrogate modeling experience from other engineering domains are encouraged to apply.

Education

• Educational Background: MS/PhD in Computer Science, Computational Engineering, Applied Mathematics, or related field preferred.
• Strong mathematical foundation in linear algebra, graph theory, and numerical methods.
• Understanding of graph theory and network analysis.