R&D Engineering, Staff Engineer

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 have spent years building software where the math actually matters, where a poorly tuned parameter or a slow convergence algorithm does not just make a demo sluggish, it blocks a customer from shipping a chip on schedule. You know that optimization is not about throwing gradient descent at a problem and hoping, it is about understanding the physics, the data, and the tradeoffs well enough to know which knobs to turn and when to stop.

You are comfortable moving between complex C++ implementations and Python prototyping, between statistical outlier analysis and conversations with field engineers who need you to explain why a model is behaving the way it is. You do not wait for perfect datasets or fully specified requirements. You dig into noisy data, ask the right questions, and build models that hold up under real-world manufacturing conditions.

The problems you like are the ones where the solution is not in a textbook. Where you have to combine numerical methods, domain knowledge, and engineering judgment to get something that works. Maybe you are finishing a PhD where you built custom optimization algorithms for physical systems, or you have a few years in industry applying advanced mathematical modeling to real engineering problems.

Either way, you bring both theoretical depth and the practical instinct to know when theory needs to bend to reality.

At Synopsys, you will work on computational lithography models that enable the next generation of semiconductor manufacturing, and the team you join will expect you to bring both rigor and creativity to problems that have never been solved before.

• Design and implement optimization techniques that reduce model calibration runtime without sacrificing accuracy, working directly with large-scale empirical datasets from leading-edge semiconductor processes
• Prototype and develop new mathematical models for lithography simulation, translating specifications into production-quality C++ code integrated into the Proteus product line
• Calibrate and tune complex non-linear model parameters using statistical analysis, numerical optimization, and domain-specific heuristics
• Collaborate with cross-functional teams including product engineering, field support, and customer‑facing teams to understand technical requirements and integrate modeling solutions into existing workflows
• Analyze outlier data points and develop sampling strategies that improve model robustness and generalization across process variations
• Maintain and extend existing computational lithography models, diagnosing performance bottlenecks and correctness issues in a large, mature codebase
• Work directly with field engineers and customers to troubleshoot model behavior, gather requirements for new features, and validate solutions against real manufacturing data

• Enable semiconductor manufacturers to bring next‑generation nodes to production faster by delivering models that calibrate in hours instead of days
• Unlock previously unsolvable lithography challenges by developing optimization techniques that handle higher complexity and tighter tolerances than existing methods
• Improve model accuracy across a wider range of manufacturing conditions, reducing costly silicon respins and accelerating time to market for customers
• Strengthen Synopsys' competitive position in computational lithography by contributing novel algorithms and methods that differentiate the Proteus platform
• Reduce customer escalations and support load by building models that are more robust, interpretable, and easier to tune in the field
• Influence product roadmap and strategy by identifying technical gaps and opportunities based on direct customer interaction and data…