HPC Software Engineer

Date posted 05/07/2026
Category Engineering Hire Type Employee Job ID 12885 Base Salary Range $101000-$151000 Remote Eligible No Date Posted 05/07/2026

We Are

Synopsys is the leader in engineering solutions from silicon to systems, enabling customers to rapidly innovate AI-powered products. We deliver industry-leading silicon design, IP, simulation and analysis solutions, and design services. We partner closely with our customers across a wide range of industries to maximize their R&D capability and productivity, powering innovation today that ignites the ingenuity of tomorrow.

You Are

You have spent your career making code run faster on hardware that keeps changing. You understand that the difference between a solver that takes three hours and one that takes twenty minutes is not just compute power, it is how the algorithm maps to memory, how threads communicate, and whether someone thought hard about cache locality six months ago. You know your way around a profiler and you can read the output without needing to Google every metric.

Working across CPU and GPU architectures does not intimidate you. You have debugged MPI communication bottlenecks, wrestled with build systems that span multiple platforms, and written code that has to perform consistently whether it is running on a local workstation or a cloud-based HPC cluster. You think in terms of scalability, not just correctness.

• Design, implement, andoptimizeparallel programming methods within Ansys Mechanical solver products using MPI, GPU programming models like CUDA, HIP, SYCL, OpenMP, and other HPC frameworks

• Profile solver performance across CPU and GPU architectures using tools like Intel VTune , 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, including on-premise clusters and cloud-based environments
• Own packaging, build system work, and DevOps tooling using CMake , Azure DevOps, Conan, Docker, or CI/CD pipelines to streamline deployment and testing workflows

• 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 evolve
• Support faster iteration cycles for product development teams by streamlining build, test, and deployment infrastructure

• MinimumRequirements: Bachelor's degree in Mechanical Engineering , Computational Science, Applied Mathematics, Physics, or related field with 2+ years of experience, or Master' s degree 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/ oneAPI , OpenMP, OpenACC , or Kokkos is a strong plus

• 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