Machine Learning Engineer – PINN/FNO & Reservoir Simulation

Simulation & ML Integration:

• Design and implement PINN-based solvers,  FNO surrogates or others to accelerate reservoir simulation and optimize subsurface workflows.
• Integrate your models into CMG’s simulation pipeline, ensuring numerical stability and scientific rigor.

Data & Pipeline Engineering:

• Build scalable data pipelines for large-scale geological and production datasets.
• Containerize and deploy inference services, wrapping PINN/FNO models with robust  APIs.

Strategic Roadmap:

• Collaborate with domain experts to define a multi-year ML/AI strategy for reservoir simulation.
• Identify key research areas and drive prototyping of next-generation ML solvers.

Early-Stage Research & Delivery:

• Lead R&D projects—from literature review and algorithm design through hands-on implementation and performance benchmarking.
• Validate model accuracy against high-fidelity simulators and real field data

Cross-Functional Collaboration:

• Pair with software engineers to productionize algorithms under clean-architecture and CI/CD best practices.
• Present findings, trade-offs, and performance metrics to stakeholders in product and subsurface teams

The above statements are intended only to describe the general nature of the job and should not be construed as an all-inclusive list of position responsibilities.