Machine Learning Engineer – PINN​/FNO & Reservoir Simulation

Join CMG’s Innovation Lab as Machine Learning Engineer with a Master’s or PhD focused on Physics-Informed Neural Networks (PINNs), Fourier Neural Operators (FNOs), Deep Reinforcement Learning (DRL) for reservoir and CFD applications. In this role you’ll blend advanced ML theory with practical reservoir modeling, driving accuracy and performance improvements from concept through production.

Key Responsibilities   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.
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 product ionize 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.
Knowledge, Skills & Experience   Academic Excellence:
Master’s or PhD in Computational Science, Mechanical/Reservoir Engineering, Applied Mathematics, or related field—particularly with a focus on PINNs, FNOs, or CFD.
Deep ML & Scientific Computing:
Proven experience implementing PINNs, FNOs, or other physics-informed architectures in Tensor Flow or PyTorch.
Desirable :
Hands-on track record with DRL—policy-gradient (PPO, TRPO), actor-critic (SAC, DDPG), or value-based methods (DQN).
Strong background in PDEs, numerical methods, and uncertainty quantification.
Software & Dev Ops

Skills:

Proficiency in Python ,C++, or other suitable languages, enabling efficient integration of AI/ML models. Familiarity with containerization (Docker) and cloud deployment (AWS/GCP/Azure) is a plus.
Analytical & Problem-Solving:
Track record of publishing or presenting research, solving complex numerical challenges, and rigorously benchmarking solutions.
Teamwork & Communication:
Comfortable collaborating across disciplines—translating deep technical work into actionable product features.
If you have the necessary qualifications, and are interested in a challenging career with us, please forward your resume in confidence to  .
No phone calls please. We thank all applicants for their interest in advance. Only those chosen for interviews will be contacted.
CMG Compensation and Benefits Overview   Why Join Us?
Competitive Package.
Research Freedom:  Access to HPC clusters, GPU farms, and open datasets to advance ML/RL research.
High Impact:  Your work will directly accelerate CMG’s simulation products and shape industry-leading digital-twin and optimization technologies.
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