R&D Engineering, Staff Engineer at Synopsys in Ottawa, ON

  • Company: Synopsys
  • Location: Ottawa, Ottawa (Hines Road), Canada
  • Job type: full time
  • Workplace: onsite
  • Posted: 2026-10-19

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Job description

Synopsys Canada ULC values the diversity of our workforce. We are committed to provide access & opportunity to individuals with disabilities and will provide reasonable accommodation to individuals throughout the recruitment and employment process. Should you require an accommodation, please contact hr-help-canada@synopsys.com . Job Description and Requirements 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 and scaling the infrastructure that makes complex engineering work possible, and you understand that the best platform is the one engineers do not have to think about. It just works, scales when it needs to, and gets out of the way. You know the difference between automation that saves five minutes and automation that changes how a team operates, and you have built both. You are comfortable sitting with an IP designer who is stuck on a Monte Carlo run that should have finished yesterday, diagnosing what broke, and walking out with a plan to prevent it from happening to the next hundred engineers. You do not wait for perfect requirements. You talk to the people doing the work, figure out where the friction is, and build something that actually helps. You have started paying attention to AI and LLMs, not because they are trendy, but because you see where they could actually make simulation setup less painful or debugging faster. You are curious enough to experiment and pragmatic enough to know when a script is still the right answer. At Synopsys, you will work on simulation platforms that support thousands of IP designers worldwide. The problems are real, the scale is significant, and what you build will directly affect how teams ship. What You'll Be Doing Build and enhance the analog/mixed-signal simulation infrastructure that supports IP design teams across the company, focusing on scalability, reliability, and usability Design and implement AI-assisted automation for simulation setup, debugging, and workflow optimization, translating emerging technologies into practical engineering tools Improve distributed and cloud-based simulation environments to increase resource utilization and reduce time-to-results for large-scale workloads Enable production sign-off flows including Monte Carlo, reliability analysis, and post-layout simulation methodologies that teams depend on for tapeout Work directly with design teams to understand their challenges, translate requirements into platform capabilities, and deliver solutions that remove friction from their workflows Partner with EDA vendors and internal tool developers to drive new features, evaluate technologies, and integrate enhancements into the platform Support engineers through documentation, training, consultation, and best-practice guidance that helps them get more out of the tools The Impact You Will Have Thousands of IP designers worldwide will use the simulation infrastructure you build, and the improvements you make will compound across every project they touch Engineers will spend less time fighting tooling and more time designing because you removed the friction points that slow them down Sign-off flows will run more reliably and finish faster, directly affecting tapeout schedules and product delivery timelines AI-driven automation you implement will change how teams approach simulation setup and debugging, setting the foundation for next-generation workflows Cloud and distributed simulation improvements you deliver will unlock capacity that was previously bottlenecked, enabling teams to run more experiments and iterate faster Your collaboration with EDA

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