Join Our Team

As an AI Reservoir Simulation Engineer, you will be at the heart of our product development, bridging the gap between traditional reservoir engineering and modern AI. You will design, develop, and implement advanced simulation workflows that leverage our AI co-pilot to solve complex subsurface challenges.

Key Responsibilities:

• Develop and validate numerical models for Chemical EOR, thermal recovery, and CO2 sequestration.
• Work with our AI team to integrate machine learning models for history matching, uncertainty quantification, and optimization.
• Design and analyze fracture models for horizontal wells using finite difference, finite element, and analytical methods.
• Provide expert support to clients, helping them apply our software to their most pressing reservoir management challenges.

Technical Requirements:

• Ph.D. in Petroleum Engineering, Chemical Engineering, or a related computational science field.
• Deep expertise in reservoir simulation using tools like Intersect, CMG, or UTCHEM.
• Strong background in solving Partial Differential Equations (PDEs) and numerical methods.
• Proficiency in Python and familiarity with C/C++ and FORTRAN.
• Experience with geothermal modeling, carbon intensity calculations, and production optimization is highly desirable.

As a Senior AI/ML Engineer, you will lead the development of the core intelligence that powers our ResSim Intelligent Simulator. You will architect and train novel machine learning models that integrate seamlessly with physics-based simulators to accelerate performance and deliver unparalleled predictive insights.

Key Responsibilities:

• Design, build, and deploy surrogate models, deep learning architectures, and reinforcement learning agents for reservoir simulation.
• Lead research on applying cutting-edge AI techniques to problems in production forecasting, well placement, and EOR design.
• Collaborate with reservoir engineers to ensure AI models are physically consistent and solve real-world operational problems.
• Optimize ML models for performance and scalability in a cloud-native, parallel computing environment.

Technical Requirements:

• Ph.D. in Computer Science, Physics, Engineering, or a related quantitative field with a focus on machine learning.
• Expert-level programming skills in Python and proficiency in C/C++ and FORTRAN.
• Demonstrated experience building and deploying ML solutions, preferably in a scientific or engineering domain.
• Strong understanding of deep learning frameworks (TensorFlow, PyTorch) and cloud computing platforms (AWS, Azure, GCP).
• Knowledge of reservoir engineering principles, CO2 sequestration, and carbon intensity calculation is a significant plus.