R&D Engineering, Staff Engineer

Date posted 06/08/2026
Category Engineering Hire Type Employee Job ID 17582 Remote Eligible Yes Date Posted 06/08/2026

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 someone who sees fluid dynamics not as an abstract academic exercise but as a set of real problems that need computational solutions that actually work. When you write Python code to model turbulence or simulate flow behavior, you are thinking about whether the results make physical sense, not just whether the script runs without errors. You have spent enough time with numerical methods to know that a simulation can be mathematically correct and still completely useless if the discretization is wrong or the boundary conditions do not reflect reality.

• Design and implement new simulation features for fluid dynamics solvers using Python and Fortran, translating research concepts into working code that produces physically accurate results
• Develop and optimize numerical solvers for computational fluid dynamics problems, balancing accuracy, stability, and performance across different flow regimes
• Build unit and integration tests to validate that your implementations produce correct results against analytical solutions, experimental data, or established benchmarks
• Investigate and resolve defects in existing simulation code, tracing issues through complex numerical algorithms and legacy Fortran implementations to deliver robust fixes
• Write technical documentation that explains your solver design choices, numerical methods, and implementation details for other engineers and researchers who will build on your work
• Prototype new computational approaches by reviewing recent literature, implementing proof-of-concept code, and evaluating whether new methods improve on existing capabilities
• Collaborate with other R&D engineers and domain experts to integrate your solver components into larger multi-physics simulation workflows

• Enable engineers designing offshore wind platforms, wave energy systems, and marine structures to simulate complex fluid dynamics scenarios they could not model before, directly supporting the global transition to renewable energy
• Accelerate critical design decisions for customers by delivering solver optimizations that cut simulation runtime from days to hours, keeping multi-million-dollar projects on schedule
• Expand the range of problems water-based industries can solve computationally by implementing new numerical methods that handle previously intractable flow regimes or boundary conditions
• Build confidence in simulation-driven design by resolving defects and delivering validation studies that prove your solvers produce results customers can trust for high-stakes engineering decisions
• Reduce the barrier to entry for new users in aquaculture, coastal engineering, and marine renewable energy sectors through clear documentation and robust solver implementations that work reliably out of the box
• Shape the future technical direction of fluid dynamics simulation as the team explores AI-accelerated methods and next-generation computational approaches
• Strengthen the reputation of Synopsys simulation tools in water-based industries by delivering software that performs accurately under real-world conditions and scales to handle the complex problems customers actually face

• You hold a PhD in engineering, physics, applied mathematics, or a related scientific discipline with research experience in computational fluid dynamics, numerical methods, or a closely related technical area
• You have strong proficiency in both Fortran and Python, with hands-on experience writing production-quality code in both languages for scientific computing or simulation applications
• You bring familiarity with numerical methods for solving partial differential equations, including discretization schemes, solver algorithms, and stability analysis
• You are comfortable with the full software development lifecycle, including version control, testing frameworks, debugging tools, and code documentation practices
• You have knowledge of fluid dynamics principles and computational methods for simulating flow behavior, or you demonstrate a strong ability to quickly learn complex physics domains through independent research and collaboration
• Experience working on solver development, performance optimization, or large-scale scientific computing projects is a strong plus
• Prior work in marine hydrodynamics, offshore engineering, renewable energy, or water-based simulation applications will set you apart

• You can take a complex physics bug that surfaces in a customer simulation, reproduce it in a test case, trace it through thousands of lines of Fortran, and explain what went wrong to both a support engineer and a domain expert without losing either of them
• You approach legacy code with curiosity rather than frustration, reading through decades-old implementations to understand the original intent before deciding how to extend or refactor
• You write tests and documentation as part of building the feature, not as an afterthought, because you have seen projects derailed by skipping those steps
• You know when to ask for clarification and when to decide with incomplete information, and you are comfortable doing both depending on what the situation requires
• You move easily between deep technical work and customer-facing conversations, switching from debugging a numerical solver to explaining a mooring analysis setup on a training call without missing a beat
• You take ownership of your work from design through deployment, following through on validation studies and customer feedback to make sure what you built solves the problem it was meant to solve