Job Description

Are you passionate about combining

machine learning and physics

to accelerate simulation and optimization in energy systems?
This is an exciting opportunity to join one of our

major global clients

as a

Machine Learning Engineer - Physics-Based Surrogate Modelling

, where you will design and deploy

Graph Neural Network (GNN)

and

physics-informed neural network (PINN)

models to emulate complex physical simulations for

pipeline and well network systems

.

The role focuses on developing scalable

surrogate models

that capture multi-physics flow dynamics, reduce simulation costs, and enhance production optimization workflows. You will work at the intersection of

data science, physics, and software engineering

, transforming simulation data into actionable machine learning frameworks for predictive performance and real-time decision support.

Key Responsibilities

Develop, train, and deploy

Graph Neural Network (GNN)

-based surrogate models to approximate physics-based simulations for pipeline and well networks. Design

data transformation pipelines

to convert numerical simulation outputs into graph representations suitable for deep learning architectures. Integrate

physics constraints and governing equations

into neural network loss functions to ensure physically consistent predictions. Implement

Physics-Informed Neural Networks (PINNs)

and

hybrid ML-physics models

for flow dynamics and pressure prediction. Collaborate with domain experts to interpret simulation data, validate model outputs, and ensure consistency with reservoir and production physics. Optimize model training for scalability, accuracy, and generalization across varying simulation conditions. Build and automate

model evaluation, versioning, and deployment frameworks

using MLOps tools. Develop visualization and post-processing tools to analyze surrogate model behavior and compare results with high-fidelity simulators. Document methodologies, validation reports, and research findings for knowledge transfer and technical review. Stay current with emerging trends in

scientific machine learning

,

GNNs

, and

physics-informed AI frameworks

.

Required Qualifications / Experience / Skills

Bachelor's or Master's degree in

Computer Science, Applied Mathematics, Petroleum Engineering, Mechanical Engineering

, or a related field (Ph.D. preferred).

Minimum 7+ years of experience

in machine learning model development, with focus on

physics-based or scientific ML applications

. Proven expertise in

Graph Neural Networks (GNNs)

using frameworks such as

PyTorch Geometric, DGL, or DeepMind Graph Nets

. Experience developing

Physics-Informed Neural Networks (PINNs)

or hybrid ML models incorporating domain constraints. Strong proficiency in

Python

and deep learning frameworks (

PyTorch, TensorFlow, or JAX

). Solid understanding of

numerical simulation, PDEs, and fluid dynamics

in pipeline or reservoir systems. Familiarity with

data preprocessing, feature engineering

, and large-scale

scientific data handling

. Hands-on experience with

MLOps tools

(MLflow, Docker, Git, CI/CD pipelines) for model deployment and version control. Ability to work cross-functionally with

simulation engineers, data scientists, and domain specialists

. Strong analytical, mathematical modeling, and problem-solving skills with attention to detail. Excellent written and verbal communication skills for technical documentation and presentations.

Job Location

100% Remote / Hybrid

Type of Employment

Full-time / Permanent

Salary: Negotiable

What You Can Expect from the Employer

Opportunity to work with a

global leader in energy and simulation technology

. Exposure to

cutting-edge AI research and scientific computing

. Competitive compensation and benefits package. Collaborative and innovation-driven environment with professional growth opportunities.
Job Types: Full-time, Permanent

Application Question(s):

Do you have 7 or more years of experience developing deep learning or physics-based ML models? Have you built or deployed Graph Neural Network (GNN) or Physics-Informed Neural Network (PINN) models in production or research settings? * Are you proficient in Python and experienced with frameworks such as PyTorch Geometric or DGL for physics-based modeling?