Synopsys software engineers are key enablers in the world of Electronic Design Automation (EDA), developing and maintaining software used in chip design, verification and manufacturing. They work on assignments like designing, developing, and troubleshooting software, leveraging the state-of-the-art technologies like AI/ML, GenAI and Cloud. Their critical contributions enable world-wide EDA designers to extend the frontiers of semiconductors and chip development.
You will architect a platform that powers the next generation of physical AI, digital twins, and simulation-driven workflows, and the decisions you make will shape how teams across the company build on top of it.
Define and drive the overall software architecture for a next-generation Physical AI platform that integrates 3D data, simulation environments, and AI-driven workflows.
Design scalable, modular systems that support both on-premise and cloud deployments, balancing performance, reliability, and maintainability.
Evaluate and integrate technologies including OpenUSD, NVIDIA Omniverse Libraries, Modulus, PhysicsNeMo, and NIMs into a cohesive platform architecture.
Translate requirements from product management, research teams, and cross-functional stakeholders into concrete technical designs and implementation roadmaps.
Lead architecture reviews, set coding standards, and establish patterns that ensure high code quality, performance, and scalability across engineering teams.
Architect deployment strategies across AWS, Azure, or GCP, leveraging containerization and orchestration technologies like Docker and Kubernetes.
Mentor engineers across the organization, providing technical leadership through design reviews, pair programming, and strategic guidance on complex problems.
Shape the architectural foundation of a platform that enables advanced simulation, digital twins, and AI-driven workflows used across Synopsys product lines.
Enable engineering teams to build faster and more reliably by establishing clear patterns, reusable components, and scalable infrastructure.
Accelerate time-to-market for Physical AI solutions by making smart integration decisions that reduce friction between research prototypes and production systems.
Improve system performance and scalability, ensuring the platform can handle increasing data volumes, user loads, and computational complexity.
Influence how Synopsys leverages emerging technologies like OpenUSD and NVIDIA Omniverse, positioning the company at the forefront of physical AI innovation.
Reduce technical debt and long-term maintenance costs by designing systems that are modular, testable, and adaptable to future requirements.
Elevate engineering practices across teams through mentorship, code reviews, and the establishment of architectural standards that raise the bar company-wide.
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