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.
What does it take to turn chip-design intent into reliable production workflows? Our Engineering Workflow Platform team builds the user-facing infrastructure touchpoints that help NVIDIA engineers create increasingly complex chips. We connect layered configuration, generated design artifacts, EDA tools, distributed jobs, validation checks, and shared project state. We combine workflow-platform architecture with hands-on systems engineering to solve challenges that conventional web-backend and CI/CD platforms do not encounter. Together, we make critical engineering workflows easier to understand, operate, and evolve safely. Ready to help shape the platform behind NVIDIA’s next generation of chips? Come build it with us!
What you'll be doing:
Design the control plane that turns engineering intent and layered configuration into repeatable workflow stages, generated artifacts, tool execution, and validation results.
Create clear models for dependencies, manifests, workflow state, retries, recovery, provenance, and machine-readable status.
Diagnose and prevent production failures involving Linux processes, environment setup, exit codes, shared filesystems, schedulers, partial writes, and stale artifacts.
Modernize established Make, Tcl, Perl, Python, shell, and YAML infrastructure while preserving behavior for active chip projects.
Partner with chip-design teams, EDA experts, and infrastructure engineers to reproduce failures, deliver compatible migrations, and improve workflow observability.
What we need to see:
Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent experience, at least 12 years of relevant software engineering experience.
Production ownership of workflow, build, release, developer-infrastructure, HPC, storage, or engineering-automation systems with multiple stages, dependencies, and generated outputs.
Strong Linux and POSIX fundamentals spanning files, permissions, symbolic links, processes, environment variables, exit codes, logs, and background jobs.
Practical programming experience in Python, C++, Perl, or a comparable language, along with shell scripting and familiarity with Make, YAML, JSON, or related infrastructure.
Experience designing configuration or state models, debugging production failures, and migrating established systems without disrupting active users.
Ways to stand out from the crowd:
Semiconductor-design or EDA workflow experience across RTL, synthesis, place-and-route, timing, signoff, ECO, or design handoffs.
Experience with LSF, Slurm, Grid Engine, shared compute infrastructure, NFS, or another shared filesystem would make an immediate impact.
Deep ownership of build systems, HPC workflows, release infrastructure, storage platforms, or other artifact-centered engineering systems would bring valuable perspective.
Experience creating structured logs, validation results, manifests, provenance records, dashboards, or actionable workflow diagnostics would help us move faster.
Success modernizing legacy Make, Tcl, Perl, Python, or shell infrastructure while maintaining compatibility, rollback, and reproducibility would set a candidate apart.
Detail you can provide in your cover letter
Describe one workflow platform or automation system you owned. What were its stages, inputs, outputs, and persistent state?
Describe a production Linux failure involving a file, process, environment variable, exit code, shared filesystem, or scheduler. What caused it and how did you fix it?
Describe a migration in which old and new behavior had to remain compatible.
Which of Make, Tcl, Perl, Python, shell, LSF, Slurm, and NFS have you used in production? Briefly describe how.
With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- B.S. or M.S. in CS, EE, CE, or equivalent experience
- 12+ years building or operating production EDA, VLSI CAD, or large engineering workflow systems
- Strong hands-on experience with RTL-to-GDS or implementation flows
- Strong Tcl and Make experience in real EDA automation environments
- Excellent Linux debugging fundamentals
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
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