System Software Engineer, Engineering Workflow Platform

Job Title

System Software Engineer, Engineering Workflow Platform

Role Summary

Build and evolve production workflow infrastructure for large-scale chip engineering. The role focuses on converting layered configuration, generated artifacts, tool execution, distributed jobs, and validation into observable, repeatable workflows.

You will work with senior platform and flow architects to modernize Tcl, Make, Perl, Python, YAML, and job-launch tooling into a clearer control-plane platform for complex engineering workflows.

Experience Level

Mid-level — typically requires 4+ years building automation, developer infrastructure, workflow platforms, distributed systems, test infrastructure, or engineering productivity tools.

Responsibilities

Deliver platform features, improve observability and diagnostics, and preserve existing workflows while enabling incremental modernization.

• Build and maintain workflow-platform features across YAML configuration, generated artifacts, Make targets, Perl/Python utilities, Tcl checks, and structured output files.
• 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 to improve inspectability and debugging.
• Implement and strengthen early-failure checks for missing files, stale generated data, invalid configuration, environment issues, scheduler problems, 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.

Requirements

Must-have skills and experience. (Formal degree information is listed under Education Requirements.)

• 4+ years building automation, developer infrastructure, workflow platforms, distributed systems, test infrastructure, or engineering productivity tools.
• Strong Linux fundamentals: shell debugging, environment setup, filesystem behavior, process execution, logs, exit codes, and background jobs.
• Practical programming experience in Python, Perl, Go, C++, or similar; comfortable reading and modifying Make, YAML, JSON, and shell-based infrastructure.
• Ability to reason 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.
• Nice-to-have: exposure to semiconductor design/EDA workflows (RTL, synthesis, place-and-route, timing, signoff, ECO, handoff); experience with workflow engines, build systems, CI/CD, job schedulers, deployment automation, data pipelines; experience improving legacy Make/Perl/shell/Tcl systems; observability, provenance tracking, and shared-filesystem troubleshooting.

Education Requirements

B.S. or M.S. in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field — or equivalent practical experience.

About the Company

Company: NVIDIA

Headquarters: Santa Clara, California, USA

NVIDIA is a global leader in accelerated computing, renowned for its innovative solutions in AI and digital twins that transform diverse industries. The company specializes in networking technologies, providing end-to-end InfiniBand and Ethernet solutions for servers and storage that optimize performance and scalability. NVIDIA serves sectors such as high-performance computing, enterprise data centers, and cloud computing, constantly reinventing its products and services to stay ahead in the market.