Software Engineer, Workflow Systems

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About the role

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.   NVIDIA's chip-design workflows coordinate long-running tools, generated design files, shared compute, and dependencies across engineering teams. We are building the software that makes these workflows repeatable, inspectable, and recoverable. Your primary focus will be workflow definitions, configuration models, and reusable tool interfaces. We work with workflow owners, chip-design specialists, and runtime engineers to deliver usable RTL-to-GDS workflows first, then extract reusable capabilities from those implementations. Runtime engineers own the underlying execution platform; this role builds the workflow software and integrations that use it. What You'll Be Doing: • Build workflow definitions, dependency models, and configuration that engineers can understand. Make final settings, their origins, and their effects explainable. • Implement workflow state and connect it to worker coordination, cancellation, retries, recovery, and resource controls. Work with runtime engineers on isolation and artifact publication so shared inputs stay protected and stale or incomplete results are rejected. • Integrate tools and schedulers through versioned interfaces with explicit inputs, outputs, resource needs, and completion checks. Expose workflow and attempt state with diagnostics that identify the failing stage and next useful action. • Improve existing Python, Go, Perl, Tcl, Make, and shell infrastructure through focused changes, compatibility tests, and staged rollout. Partner with workflow owners to validate complete scenarios, reproduce failures, and support production adoption. • Use AI development tools effectively. Review generated code, independently verify expected test results, and own the behavior shipped to users. What We Need To See: • BS/MS in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience. 4+ years developing production software, or equivalent demonstrated scope. • Strong programming in Go, Python, C++, Java, Rust, or another suitable language, with the ability to learn the team's languages. Demonstrate clear interfaces, state models, meaningful tests, and reasoned implementation tradeoffs. • Experience with workflows, build systems, job execution, release systems, scientific or ML infrastructure, controllers, or other software that manages dependent work and persistent results. • Concrete reasoning about retries, concurrent activity, incomplete outputs, process failure, and safe changes to systems already in use • Ability to learn an unfamiliar domain, make implementation decisions independently, and carry a change through verification and delivery. Ways To Stand Out From The Crowd: • Owned worker or scheduler integrations, workflow execution, isolation, or recovery mechanisms under real failure conditions. • Built schemas, configuration layering, compilers, build graphs, or APIs that other engineers successfully extended. • Implemented reproducible runs, artifact manifests, lineage, or cache invalidation. Modernized mature systems with compatibility tests, shadow comparisons, contr

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