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# System Software Engineer, Engineering Workflow Platform

- **Company:** NVIDIA Corporation
- **Location:** Santa Clara, CA, United States
- **Experience:** Experienced
- **Salary:** $152,000.0 - $241,500.0
- **Contract:** Permanent contract
- **Skills:** Computer-Aided Design, Artificial Intelligence, Systems Engineering, Bash Shell, C++ (Programming Language), Computer Programming, System Configuration, Continuous Delivery, Continuous Integration, Software Debugging, Linux, File Systems, Distributed Systems, Perl (Programming Language), Home Automation, JSON, Job Scheduling, Python (Programming Language), Unix Shell, Productivity Software, Software Engineering, System Testing, Tcl (Programming Language), YAML, Scripting, Grafana, Integration Tests, Deployment Automation, Build Tools, Data Management, Physical Design, Data Pipelines, Legacy Systems, Golang
- **Published:** September 22, 2026
- **Apply:** https://www.careerbuilder.com/job-details/system-software-engineer-engineering-workflow-platform-santa-clara-ca--0c66aa77-78ef-4f32-9d0d-e8dc60e55cf2

## About the Role

* B.S. or M.S. in CS, EE, CE, or equivalent experience
 * 4+ years building automation, developer infrastructure, workflow platforms, distributed systems, test infrastructure, or engineering productivity tools
 * Strong Linux fundamentals, including shell debugging, environment setup, filesystem behavior, process execution, logs, exit codes, and background jobs
 * Practical programming experience in Python, Perl, Go, C++, or similar, and comfortable reading and modifying Make, YAML, JSON, and shell-based infrastructure
 * Ability to reason carefully about configuration layers, generated files, schemas, validation rules, compatibility, and incremental migration of legacy systems
 * Strong debugging habits, clear written communication, and experience improving production infrastructure without destabilizing active users

Ways To Stand Out From The Crowd:

 * Exposure to semiconductor design or EDA workflows, especially RTL, synthesis, place-and-route, timing, signoff, ECO, or handoff flows
 * Background with workflow engines, build systems, CI/CD platforms, job schedulers, deployment automation, data pipelines, or large-scale engineering automation
 * Experience improving legacy Make, Perl, shell, Python, or Tcl systems while preserving existing behavior
 * Experience creating structured logs, JSON/YAML schemas, validation frameworks, provenance tracking, dashboards, or observability tools
 * Background with shared filesystems, partial writes, stale state, locking, reproducibility, generated artifacts, batch jobs, tests, migrations, documentation, or debug tooling, Artificial Intelligence (AI), Automation, C++ Programming Language, Communication Skills, Continuous Deployment/Delivery, Continuous Integration, Data Management, Debugging Skills, Debugging Tools, Distributed Computing, Documentation, File Systems, Go Programming Language (Golang), Home Automation, Integration Testing, JSON, Linux Operating System, Perl Programming Language, Place and Route, Python Programming/Scripting Language, Reporting Dashboards, Scripting (Scripting Languages), Semiconductors, Software Engineering, System Migration, System Test, Systems Engineering, Tcl-Tk, Unix Shell Programming, Validation Testing

## Description

NVIDIA is building the next generation of production workflow infrastructure for large-scale chip engineering. This platform turns intent, layered configuration, generated files, tool execution, distributed jobs, validation checks, and shared project state into observable, repeatable workflows.

This role is for a systems-minded engineer who may not have prior chip-design or CAD-flow experience, but has strong fundamentals in Linux, automation, configuration systems, scripting, and production infrastructure. You will work alongside senior platform and flow architects to evolve existing Tcl, Make, Perl, Python, YAML, and job-launch infrastructure into a clearer control-plane platform for complex engineering workflows.

What You'll Be Doing:

 * Build and maintain workflow-platform features across YAML configuration, generated artifacts, Make targets, Perl/Python utilities, Tcl checks, and structured output files
 * Help model workflow stages, inputs, outputs, validation signals, generated files, dependencies, status, and ownership in configuration and manifests
 * Create machine-readable check results, run manifests, provenance records, log summaries, and status outputs that make behavior easier to inspect and debug
 * Strengthen early-failure checks for missing files, stale generated data, invalid configuration, bad environment setup, scheduler issues, and incomplete run state
 * Add and test integrations with distributed job execution, shared compute, filesystem state, data-fidelity tracking, and dependency tracing
 * Work with senior engineers and users to reproduce failures, trace configuration behavior, improve diagnostics, update documentation, and preserve existing workflows


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