Design, implement, andoptimizeparallel programming methods within Ansys Mechanical solver products using MPI, GPU programming models like CUDA, HIP, SYCL, Open. MP, and other HPC frameworks. Profile solver performance across CPU and GPU architectures using tools like Intel. V - Tune, NVIDIA Nsight, or similar, and translate findings into actionable performance improvements. Build andmaintaincode benchmarking suites that track solver performance across releases and catch regressions before they ship. Drive adoption of modular, hardware-agnostic HPC programming models across multiple solver codebases, working with development teams to ensure consistency and reusability. Collaborate with numerical methods experts to translate complex algorithmic requirements into performant, maintainable software designs. Support procurement, configuration, and management of HPC development and testing platforms, includingon-premiseclusters and cloud-based environments. Own packaging, build system work, and DevOps tooling using. C - Make, Azure DevOps, Conan, Docker, or CI/ CD pipelines to streamline deployment and testing workflows. The Impact You Will Have. Reduce solve times for engineering simulations used by leading companies across automotive, aerospace, energy, and electronics industries. Enable customers to run larger, more complex models by making solvers scale efficiently across hundreds or thousands of cores. Accelerate the adoption of GPU computing in production simulation workflows, unlocking new performance tiers for users with modern hardware. Improve developer productivity across multiple solver teams by building reusable HPC frameworks and shared tooling. Ensure performance consistency and reliability across solver releases through rigorous benchmarking and regression testing. Help shape the technical direction of Synopsys simulation products as HPC architectures and customer workloads continue to evolveSupport faster iteration cycles for product development teams by streamlining build, test, and deployment infrastructure. What You'll Need. Minimum. Requirements:Bachelor's degree in. Mechanical Engineering,Computational Science,Applied Mathematics, Physics, or related field with 2 years of experience, or. Master'sdegree in a related field. PhD preferred. Strong hands-on experience with HPC software design, testing, and deployment in production or research environments. Solid understanding of data structures, algorithms, and performance considerations in parallel computing contexts. Proficiencywith Git and collaborative development workflows across distributed teams. Proficiencyin Fortran and C/ C for performance-critical code development. Experience with MPI and distributed memory programming models. Experience with GPU hardware and at least one GPU programming model such as CUDA, HIP, SYCL/one. API, Open. MP,Open. ACC, or. Kokkosis a strong plus. Who You Are. You can look at a profiler trace andidentifythe bottleneck without needing three meetings to discuss it. You write code that other engineers can pick up six months later without needing you to explain every design choice. You ask clarifying questions when a requirement is vague rather than guessing and building the wrong thing. You are comfortable managing your own time across multiple priorities and know when to escalate blockers versus solve them yourself. You can explain a technical tradeoff between two HPC approaches to a domain expert in terms they care about, not just what the benchmark says. You stay current with HPC architecture trends and programming models without needing to be told, because you care about building software that will still perform well two hardware generations from now. The Team You'll Be Part Of. You will work within the Ansys Mechanical R&D organization, collaborating with numerical methods experts, solver developers, and other HPC engineers across a geographically distributed team. Your work will directly support the performance and scalability of industry-leading simulation products used by engineers worldwide to design and validate critical systems.