R&D Engineering, Sr Staff Engineer (f/m)

Date posted 05/19/2026
Category Engineering Hire Type Employee Job ID 17546 Remote Eligible No Date Posted 05/19/2026

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 are a mathematician who lives at the edge of what's solvable, where theory meets real-world physics constraints and computational limits. You have spent years turning PDEs into methods that actually converge, not just in textbooks but in production solvers running on massive meshes under impossible deadlines. The thrill for you is not just proving a method works, it is watching it unlock a simulation that was previously out of reach.

You move fluidly between deep theory and practical implementation. One day you are reading the latest paper on adjoint-based optimization, the next you are debugging why a gradient calculation blows up at iteration 47 in a coupled thermal-structural case. You know that elegance matters, but so does robustness, and you have learned to balance both without compromising either.

Working across disciplines energizes you. You can talk to a fluids developer about turbulence models and a structural engineer about contact nonlinearity, and you find the common mathematical thread that makes both problems tractable. At Synopsys, you will work on solvers that power engineering decisions across industries, and the methods you develop will become part of tools used worldwide.

What You'll Be Doing

• Develop and implement advanced optimization algorithms for CFD, structural mechanics, and multiphysics problems within Ansys Mechanical and Ansys Fluent solver frameworks
• Research and prototype cutting-edge numerical methods for PDE-constrained optimization, including adjoint methods, gradient-based techniques, and topology optimization
• Collaborate directly with solver development teams across international sites to integrate mathematical innovations into production code
• Debug complex numerical issues in large-scale simulations, tracing problems from user-reported failures back to core algorithmic assumptions
• Track and evaluate emerging research in computational optimization, multiphysics coupling, and numerical analysis to identify opportunities for solver enhancement
• Contribute to technical roadmaps by translating mathematical possibilities into engineering features that solve real customer problems
• Write and maintain high-performance C/C++ or Python code that balances numerical accuracy, computational efficiency, and long-term maintainability

• Enable engineers worldwide to optimize designs that were previously too computationally expensive or numerically unstable to solve
• Accelerate time-to-solution for multiphysics optimization problems by orders of magnitude through algorithmic breakthroughs
• Expand the range of physics and coupling scenarios that Ansys solvers can handle reliably at scale
• Reduce the expertise barrier for optimization by making advanced methods robust enough for everyday engineering use
• Influence the technical direction of flagship simulation products used across aerospace, automotive, energy, and electronics industries
• Build mathematical foundations that other developers across the Engineering Simulation group will build upon for years
• Shape how the next generation of physics-based design tools approach optimization under real-world constraints

• PhD in Applied Mathematics, Computational Mathematics, or closely related field with focus on numerical methods and optimization
• Deep hands-on experience with multiphysics PDE-constrained optimization, including adjoint methods, shape optimization, or topology optimization in practical applications
• Strong programming skills in C/C++ or Python, with demonstrated ability to implement complex numerical algorithms in production-quality code
• Proven ability to collaborate effectively across distributed international teams and manage contributions to multiple concurrent projects
• Advanced analytical and debugging skills, particularly in tracing numerical failures to root mathematical causes in large-scale systems
• Experience with Ansys Fluent, Ansys Mechanical, or comparable large-scale commercial simulation platforms is a strong plus
• Track record of applying research-level mathematics to solve real engineering problems, whether through publications, patents, or shipped features

• You can explain a complex optimization algorithm to a mechanical engineer in terms they can use, and then turn around and defend the mathematical rigor to a PhD reviewer
• When a solver fails in a way no one has seen before, you get curious rather than frustrated, and you systematically work backward until you find the assumption that broke
• You read recent conference papers not to collect citations but to find ideas you can actually use next quarter
• You are comfortable saying "this approach won't scale" or "we need a different formulation" when you see a mathematical dead end, even if it means restarting work
• You balance perfectionism with pragmatism, knowing when a method is good enough to ship and when it needs another iteration
• You thrive in technical discussions where half the room is talking about physics and the other half is talking about linear algebra, and you are the bridge between them