PMy client and I are looking for a highlight experience Senior Optimisation Engineer in a highly impactful role architecting and deploying advance nonlinear optimisation engines that define theoretical limits of out lithography systems. /ppbr/ppYou will be responsible for defining how we solve high-dimensional, nonlinear problems, mentoring the team on advanced numerical methods, and establishing the standards for optimization reliability. Furthermore, you will be working alongside multifunctional, high performing teams. /ppbr/ppbWhat you’ll do: /b /pullibAdvanced Algorithm Engineering: /b Design and engineer custom, large-scale nonlinear optimization algorithms. /lilibComplex Mathematical Formulation: /b Translate intricate multi-physics phenomena into rigorous, differentiable optimization problems. /lilibDeep Solver Integration Customization: /b Go beyond basic API interfacing to deeply customize solver behaviours. You will modify linear algebra backends, Hessian approximations, and callback structures to maximize solver performance. /lilibCode Implementation: /b Write highly optimized, modular, and testable scientific code in Julia, Python, or C++. /lilibGPU-Accelerated Optimization: /b Implement distributed and GPU-accelerated optimization routines. You will apply parallelization strategies (MPI, CUDA/Kernel abstractions) required to solve problems with millions of degrees of freedom in viable timeframes. /lilibAlgorithmic Tuning Profiling: /b Conduct deep-dive performance analysis on critical loops. You will profile memory access patterns and algorithmic complexity to eliminate bottlenecks and resolve numerical instabilities. /lilibTesting and Validation: /b Design comprehensive validation frameworks that stress-test optimization convergence. /li /ulpbr/ppbr/ppbWho we are looking for: /b /pulliPhD in Applied Mathematics, Computer Science, Computational Physics, or Operations Research /liliDeep command of nonlinear optimization theory, including KKT conditions, duality, and the mechanics of Interior Point or Trust-Region methods. /liliAdvanced proficiency in Julia, C++, or Python /liliDeep knowledge of sparse matrix factorizations, iterative linear solvers, and preconditioning techniques. /liliHands-on experience tuning and interfacing with large-scale solvers (e.g., IPOPT, KNITRO, SNOPT) at a low level. /liliAbility to diagnose debugging and stability /liliStrong discipline in modern software practices, including version control (Git), unit testing for numerical code, and CI/CD workflows. /liliExposure to GPU programming, high-performance or parallel algorithms /li /ulpbr/ppbr/ppbr/ppHow does this opportunity sound to you? /ppbr/ppFollowing your application, Amelia Pudney will discuss the opportunity with you in detail. She will happily answer any queries surrounding the opportunity and the potential for career growth. This position is highly popular and has possibility to close prematurely; apply as soon as possible to avoid disappointment. Please select 'apply', alternatively email for any further information. /p