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.
Descubra más sobre las tareas diarias, las responsabilidades generales y la experiencia requerida para esta ocasión desplazándose hacia abajo ahora.
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.
What you'll do:
Advanced Algorithm Engineering: Design and engineer custom, large-scale nonlinear optimization algorithms.
Complex Mathematical Formulation: Translate intricate multi-physics phenomena into rigorous, differentiable optimization problems.
Deep Solver Integration & Customization: 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.
Code Implementation: Write highly optimized, modular, and testable scientific code in Julia, Python, or C++.
GPU-Accelerated Optimization: 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.
Algorithmic Tuning & Profiling: Conduct deep-dive performance analysis on critical loops. You will profile memory access patterns and algorithmic complexity to eliminate bottlenecks and resolve numerical instabilities.
Testing and Validation: Design comprehensive validation frameworks that stress-test optimization convergence. Who we are looking for:
Ph D in Applied Mathematics, Computer Science, Computational Physics, or Operations Research
Deep command of nonlinear optimization theory, including KKT conditions, duality, and the mechanics of Interior Point or Trust-Region methods.
Advanced proficiency in Julia, C++, or Python
Deep knowledge of sparse matrix factorizations, iterative linear solvers, and preconditioning techniques.
Hands-on experience tuning and interfacing with large-scale solvers (e.g., IPOPT, KNITRO, SNOPT) at a low level.
Ability to diagnose debugging and stability
Strong discipline in modern software practices, including version control (Git), unit testing for numerical code, and CI/CD workflows.
Exposure to GPU programming, high-performance or parallel algorithms How does this opportunity sound to you?
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. xqbhyrx Please select 'apply', alternatively email for any further information.