Machine Learning Engineer - PINNs/FNO We are seeking a highly skilled Machine Learning Engineer to join our Innovation Lab team at Computer Modelling Group (CMG). In this role, you will work on the development and implementation of cutting-edge machine learning algorithms for reservoir simulation and optimization. The successful candidate will have a strong background in physics-informed neural networks (PINNs), Fourier neural operators (FNOs), and deep reinforcement learning (DRL) for reservoir and computational fluid dynamics (CFD) applications. You will be responsible for designing and implementing PINN-based solvers, FNO surrogates, or other ML models to accelerate reservoir simulation and optimize subsurface workflows. You will also integrate your models into CMG's simulation pipeline, ensuring numerical stability and scientific rigor. Additionally, you will build scalable data pipelines for large-scale geological and production datasets and containerize and deploy inference services, wrapping PINN/FNO models with robust APIs. The ideal candidate will have a Master's or PhD in Computational Science, Mechanical/Reservoir Engineering, Applied Mathematics, or a related field, particularly with a focus on PINNs, FNOs, or CFD. They will have proven experience implementing PINNs, FNOs, or other physics-informed architectures in TensorFlow or PyTorch and a strong background in PDEs, numerical methods, and uncertainty quantification. The successful candidate will be a team player who is comfortable collaborating across disciplines, translating deep technical work into actionable product features. They will also have excellent analytical and problem-solving skills, with a track record of publishing or presenting research, solving complex numerical challenges, and rigorously benchmarking solutions.