System Software Engineer, Engineering Workflow Platform

Job Title

System Software Engineer, Engineering Workflow Platform

Role Summary

Join the team building production workflow infrastructure for large-scale chip engineering. The platform coordinates configuration, generated artifacts, tool execution, distributed jobs, validation checks, and project state into observable, repeatable engineering workflows.

This role is systems-focused: you will work with platform and flow architects to evolve existing Make/Tcl/Perl/Python/YAML and job-launch infrastructure into a clearer control-plane for complex engineering workflows.

Experience Level

Mid-level — the role expects experienced engineers (the posting requests 4+ years of relevant experience).

Responsibilities

Primary responsibilities include designing, implementing, and maintaining workflow-platform features and improving reliability and observability for engineering runs.

• Build and maintain 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 aid inspection and debugging.
• Improve early-failure checks for missing files, stale generated data, invalid configuration, environment issues, scheduler problems, and incomplete run state.
• Integrate and test with distributed job execution, shared compute and filesystems, data-fidelity tracking, and dependency tracing.
• Work with senior engineers and users to reproduce failures, trace configuration behavior, update diagnostics and documentation, and preserve existing workflows.

Requirements

Must-have technical skills and experience for successful candidates.

• Must-have: 4+ years building automation, developer infrastructure, workflow platforms, distributed systems, test infrastructure, or engineering productivity tools.
• Must-have: Strong Linux fundamentals (shell debugging, environment setup, filesystem behavior, process execution, logs, exit codes, background jobs).
• Must-have: Practical programming experience in Python, Perl, Go, C++, or similar; comfortable reading and modifying Makefiles, YAML, JSON, and shell-based infrastructure.
• Must-have: Ability to reason about configuration layers, generated files, schemas, validation rules, compatibility, and incremental migration of legacy systems.
• Must-have: Strong debugging habits and clear written communication; experience improving production infrastructure without destabilizing active users.
• Nice-to-have: Exposure to semiconductor design or EDA workflows (RTL, synthesis, place-and-route, timing, signoff, ECO, handoff flows).
• Nice-to-have: Background with workflow engines, build systems, CI/CD platforms, job schedulers, deployment automation, data pipelines, or large-scale engineering automation.
• Nice-to-have: Experience improving legacy Make, Perl, shell, Python, or Tcl systems while preserving existing behavior; experience creating structured logs, validation frameworks, provenance tracking, or observability tools.

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.