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 years building software where the geometry matters, where a mesh that looks fine in a demo falls apart under real loads. You know that meshing is not just discretization, it is the foundation that everything downstream depends on, and you have learned to balance mathematical rigor with performance constraints that actually ship.

You are comfortable moving between C++ performance work, C# tooling, and Python scripting. Lately, you have been experimenting with LLMs, RAG pipelines, or agentic workflows because you see real potential to accelerate how engineers interact with simulation workflows. You do not need a perfect spec to get started. You ask the right questions and find a path through ambiguity without creating technical debt.

Debugging does not intimidate you. Whether it is a segfault in a million-line C++ codebase or a model that behaves differently on GPU versus CPU, you dig in and figure it out. At Synopsys, you will work on meshing capabilities that power mechanical simulation products, and what you build will directly affect how engineers solve real-world problems.

What You'll Be Doing

• Design, develop, and maintain core meshing algorithms and frameworks in C++ and C# that support mechanical simulation workflows across Synopsys products
• Integrate meshing capabilities into flagship mechanical analysis products, working closely with product, solver, and platform engineering teams
• Build and prototype GenAI-powered features using LLMs, RAG, MCP, Langchain, or Langgraph to improve simulation setup, automation, or user workflows
• Debug and optimize complex meshing code across Windows and Linux environments, including low-level performance tuning and GPU acceleration where applicable
• Collaborate with mechanical engineers and domain experts to ensure meshing outputs meet the needs of structural, fluids, and thermal analysis use cases
• Contribute to software design and architecture decisions that shape the long-term direction of meshing infrastructure
• Work with minimal supervision, owning features end to end from design through deployment and maintenance

The Impact You Will Have

• Your meshing algorithms will directly influence the accuracy, speed, and reliability of simulations used by engineers solving real-world mechanical and fluids problems
• The GenAI prototypes you build will shape how Synopsys integrates intelligent automation into simulation workflows, potentially redefining how users interact with complex CAE tools
• Your architectural decisions will set the foundation for meshing capabilities that scale across multiple products and platforms
• The performance optimizations you implement will reduce solve times and improve user experience for engineers running large-scale simulations
• Your collaboration across teams will ensure meshing infrastructure evolves in lockstep with solver, geometry, and platform advancements
• The quality and maintainability of your code will enable future engineers to build on solid ground rather than work around legacy constraints
• Your technical leadership will help define best practices for meshing, DevOps, and AI integration within the Synopsys simulation ecosystem

What You'll Need

• Bachelor's or Master's in Computer Science, Mechanical Engineering, Computational Engineering, or equivalent practical experience
• 7+ years of hands-on software development experience with strong proficiency in C++ for performance-critical systems
• Demonstrated experience with meshing algorithms, computational geometry, or CAE software development
• Working knowledge of C# and Python for tooling, scripting, or workflow automation
• Solid grounding in software design, architecture, debugging, and development in large, complex codebases
• Experience with GenAI technologies such as LLMs, RAG pipelines, MCP, Langchain, or Langgraph is a strong plus

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