Overview One of the world's largest providers of products and services to the energy industry has a need to develop and support enterprise information system in Oil & Gas domain.
Responsibilities We are looking for a Software Engineer to contribute to the development of a global optimization capability for petroleum surface networks within the NEXUS reservoir simulator. This role is product-focused and centers on building, integrating, and hardening a Bayesian Optimization (BO)–based engine that complements the existing gradient-based GRG optimizer. The feature enables reservoir and production engineers to identify more robust and globally optimal well control strategies—such as gas-lift allocation and choke settings—when dealing with highly non-linear and multimodal network behavior. You will work as part of a product engineering team, collaborating closely with domain experts, QA, and platform engineers to deliver maintainable, performant, and user-ready functionality into a production-grade simulator. What You’ll Work On: Implement and integrate Bayesian Optimization workflows within the existing NEXUS surface network optimization framework; Develop and maintain Gaussian Process–based surrogate models used during optimization runs; Integrate acquisition functions that support reliable decision-making across challenging objective landscapes; Implement initial sampling strategies to ensure stable and repeatable optimization outcomes; Contribute to performance tuning, memory efficiency, and parallel execution in large simulation workloads; Improve code robustness, testability, and maintainability in a long-lived product codebase.
Qualifications Must have Programming & Implementation: Fortran 90/95: Experience working with modules, allocatable arrays, and structured Fortran code; Ability to modify and extend existing numerical code responsibly; Numerical Computing: Numerical linear algebra fundamentals; Practical use of Cholesky decomposition and triangular solves; Experience using LAPACK routines; Awareness of numerical stability and basic conditioning issues; Optimization & Modeling: Gaussian Process regression (practical level): Understanding of GP concepts (mean, covariance, kernels); Ability to implement or adapt GP prediction code with guidance; Bayesian Optimization concepts: Familiarity with acquisition functions such as Expected Improvement (EI) and Upper Confidence Bound (UCB); Understanding of exploration vs. exploitation from a user-outcome perspective; Sampling & Experiment Design: Latin Hypercube Sampling (LHS); Experience implementing or using space-filling designs for initial parameter exploration; Product & Engineering Expectations: Focus on reliability, usability, and repeatability rather than experimental novelty; Write clear, maintainable code aligned with existing platform standards; Ensure features behave predictably across a wide range of customer models; Collaborate with QA and support teams to diagnose and resolve field issues; Be mindful of performance, memory usage, and MPI execution constraints.
Nice to have: Exposure to reservoir simulation or production optimization domains; Experience working on large, long-lived scientific software products; Familiarity with user-facing configuration workflows or optimizer settings.
#J-18808-Ljbffr