Date posted 05/25/2026
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 performance is not negotiable and where the wrong algorithm choice shows up immediately in runtime benchmarks or memory profiles. Graph algorithms are not abstract theory to you, they are tools you reach for when modeling netlists, dependency chains, or optimization problems that actually ship in production EDA workflows.
You write C++ that other engineers can maintain, not because you follow a style guide religiously, but because you have debugged enough legacy code to know what clarity costs when it is missing. When a system slows down under scale, you do not guess, you profile, you trace, and you fix the right thing.
EDA tools might be new territory for you, or they might be familiar ground. Either way, you are curious about how RTL becomes silicon and how software shapes that process. You ask good questions when requirements are thin. You collaborate without needing to own every decision. You know that the best solutions come from understanding the problem deeply, not from writing clever code quickly.
At Synopsys, you will work on tools that semiconductor companies depend on to design the chips that power everything. The work is hard, the codebase is real, and the problems you solve will matter.
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Design and implement algorithms for graph processing, optimization, and data structure management within EDA tool workflows
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Write performance-critical C++ code that handles large-scale design data, netlist processing, or analysis pipelines
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Debug and optimize existing systems to improve runtime, memory usage, and scalability across complex workloads
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Collaborate with RTL designers, architects, and cross-functional engineering teams to align tool capabilities with real design challenges
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Contribute to the development and enhancement of EDA tools used internally and by customers for semiconductor design
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Evaluate and integrate modern development tools, including AI-assisted coding platforms like GitHub Copilot or Cursor, to improve team productivity
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Review code, mentor peers, and contribute to engineering best practices across the R&D organization
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Your algorithms will directly improve the speed and accuracy of EDA tools used to design cutting-edge semiconductor products
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The code you write will scale to handle designs with millions of nodes, gates, and interconnects, enabling faster time to market for chip development
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Your optimizations will reduce tool runtime and memory footprint, making complex design workflows more efficient for engineers worldwide
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The systems you build will support next-generation AI, automotive, and high-performance computing chip designs
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Your contributions will help Synopsys maintain its leadership in EDA technology and deliver tools that set industry standards
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The collaboration you foster between software and hardware teams will lead to better tool design and more effective solutions
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Your work will shape how semiconductor companies approach design automation, verification, and optimization at scale
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Bachelor's or Master's in Computer Science, Electronics Engineering, or equivalent practical experience
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5-8 years of professional software development experience with a focus on systems programming or algorithmic work
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Deep expertise in data structures and graph algorithms, you have implemented and optimized them in production systems
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Advanced proficiency in C++ for performance-critical software development
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Strong problem-solving skills and a solid grounding in software design principles
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Experience in EDA tools, semiconductor design workflows, or compiler design is a strong plus
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Familiarity with RTL languages like Verilog or VHDL, or exposure to AI-assisted coding tools like GitHub Copilot or Cursor, is a plus
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You can explain a complex algorithmic tradeoff to a hardware engineer in two sentences without losing the technical nuance
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You do not wait for perfect specs, you clarify what is missing, propose a reasonable path forward, and iterate as you learn more
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When a performance issue surfaces, you dig into profiling data and trace logs instead of guessing at the root cause
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You are comfortable reviewing someone else's code and giving feedback that makes the system better without making the conversation uncomfortable
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You stay current with tools and techniques that make you more effective, whether that is a new profiler, a better debugger, or an AI coding assistant
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You know that good software is not just correct, it is maintainable, and you write with the next engineer in mind
Your recruiter will
