P My 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. /p p You 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. /p pbu What you’ll do: /u /b /pullib Advanced Algorithm Engineering: /b Design and engineer custom, large-scale nonlinear optimization algorithms. /lilib Complex Mathematical Formulation: /b Translate intricate multi-physics phenomena into rigorous, differentiable optimization problems. /lilib Deep 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. /lilib Code Implementation: /b Write highly optimized, modular, and testable scientific code in Julia, Python, or C++. /lilib GPU-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. /lilib Algorithmic 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. /lilib Testing and Validation: /b Design comprehensive validation frameworks that stress-test optimization convergence. /li /ul pbu Who we are looking for: /u /b /pulli PhD in Applied Mathematics, Computer Science, Computational Physics, or Operations Research /lili Deep command of nonlinear optimization theory, including KKT conditions, duality, and the mechanics of Interior Point or Trust-Region methods. /lili Advanced proficiency in Julia, C++, or Python /lili Deep knowledge of sparse matrix factorizations, iterative linear solvers, and preconditioning techniques. /lili Hands-on experience tuning and interfacing with large-scale solvers (e.g., IPOPT, KNITRO, SNOPT) at a low level. /lili Ability to diagnose debugging and stability /lili Strong discipline in modern software practices, including version control (Git), unit testing for numerical code, and CI/CD workflows. /lili Exposure to GPU programming, high-performance or parallel algorithms /li /ul p How does this opportunity sound to you? /p p Following 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 #39;apply#39;, alternatively email for any further information. /p