Ml Engineer (Physics-Informed / GNN)

TL;DR

Ml Engineer (Physics-Informed / GNN): Develop advanced machine learning models using Graph Neural Networks and Physics-Informed Neural Networks for the oil and gas industry. Focus on designing surrogate models that predict flow dynamics and pressure distributions in pipeline networks with an accent on integrating physical laws and building high-precision predictive models.

What you will do

• Design and implement neural network architectures modeling flow dynamics in pipeline networks.
• Build surrogate models predicting pressure distributions and flow rates under various scenarios.
• Create data pipelines transforming physics-based simulation results into graph representations.
• Develop training frameworks incorporating physics constraints into neural network loss functions.
• Collaborate with petroleum engineers to align model predictions with physical and operational constraints.
• Implement model monitoring, validation, and continuous improvement workflows.

Requirements

• English: B2 level or higher required
• 5+ years experience in ML engineering, GNN specialization, or related research roles.
• Expertise in Graph Neural Networks and deep learning frameworks like PyTorch Geometric, DGL, or TensorFlow GNN.
• Experience with Physics-Informed Neural Networks and surrogate modeling for engineering applications.
• Strong Python skills and familiarity with scientific computing libraries (NumPy, SciPy, Pandas).
• Ability to work with complex data structures such as graphs, time series, and spatial data.

Culture & Benefits

• Flexible full-time schedule with remote work from anywhere.
• On-site onboarding immersion in Kuwait with expenses covered.
• Health insurance, sports compensation, and corporate discounts.
• Mentoring, internal knowledge sharing, and corporate English courses.
• Career development pathways and corporate events for employees and families.

Hiring process

• Recruiter interview and English check (30 minutes).
• Technical interview with company experts (1 hour).
• Client-side interview conducted in English.
• Final HR interview discussing work conditions and salary.