18 hours ago Be among the first 25 applicants Direct message the job poster from Computer Modelling Group 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 productionize algorithms under clean-architecture and CI/CD best practices. Present findings, trade-offs, and performance metrics to stakeholders in product and subsurface teams Note: This description reflects the general nature of the role. It’s not a complete list of responsibilities—we believe in flexibility, initiative, and growing together. 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 TensorFlow 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 & DevOps 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. 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. No need to call us about the status of your application. We promise—we’re reviewing every submission, and if your skills are a match, you’ll hear from us! We kindly request that external recruiters and agencies refrain from submitting unsolicited resumes or candidate profiles. Submissions without a signed agreement in place will not be considered and will become the property of CMG. Seniority level Seniority level Mid-Senior level Employment type Employment type Full-time Job function Job function Administrative Industries Software Development Referrals increase your chances of interviewing at Computer Modelling Group by 2x Get notified about new Machine Learning Engineer jobs in Bogotá, Capital District, Colombia . 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