Machine Learning Engineer (onsite work in Kuwait)

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 for first 6-12 months. On-site

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

Technical Domain Knowledge:

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

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:

Oil & gas industry experience is a strong plus, However, candidates with relevant surrogate modeling experience from other engineering domains encouraged to apply

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