We are seeking a Linux Systems Engineer to join our Infrastructure Engineering team, dedicated to supporting high-performance EDA compute farms. You will be responsible for designing, deploying, and maintaining enterprise-grade Linux environments across RPM-based distributions, optimized for the high-throughput requirements of EDA workloads. You will drive automation initiatives, and build robust tooling to support a highly available and scalable infrastructure.
What you'll be doing:Linux Infrastructure Engineering: Maintain, tune, and secure mission-critical Linux operating systems across production and non-production environments.
End-to-End Automation: Lead infrastructure configuration management and continuous deployment workflows using Infrastructure as Code, e.g. Ansible and Terraform.
Tooling Development: Write high-quality, maintainable code in Python, Bash, or Go to automate repetitive operational tasks and integrate infrastructure telemetry.
Naming & Storage Services: Manage Linux Naming Services (LDAP/SSSD) and networked storage (NFS, Autofs) environments to support high-performance EDA workloads.
Linux Internals: 8+ years of deep systems engineering experience working with RPM-based Linux distributions. Bachelor's degree or equivalent work experience.
Infrastructure as Code: Strong track record of automating end-to-end multi-tier infrastructure configurations and managing centralized deployments. Experience with more than one configuration management system is a bonus.
Virtualization and Container Architecture: Deep understanding of virtualization and container internals (e.g., Docker, Podman, Kubernetes, MicroVMs), including trade-offs between technologies. Demonstrated ability to design and build scalable container infrastructure from scratch, including registry management, custom image development, and layer optimization.
Scripting & Tooling: Proficiency in at least one modern language (Python, Bash, or Go) for infrastructure automation, tooling development, and API integrations.
Networking Fundamentals: Deep operational understanding of OSI layers, TCP socket states, MTU optimization, packet captures (tcpdump, wireshark), and modern physical or software-defined networking (SDN).
Experience with CI/CD integration pipelines (GitLab CI, GitHub Actions, or Jenkins) for infrastructure artifacts. Relevant industry certifications (e.g., RHCE, CKA).
Proven experience managing GCP and AWS compute services Experience with using LLMs to build and manage infrastructure
Prior experience supporting EDA toolchains (e.g., Synopsys, Cadence) and high-performance compute clusters (e.g. exposure to LSF and Slurm environments a bonus)
Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. We have some of the most forward-thinking and talented people in the world working for us and, due to outstanding growth, our world-class engineering teams are growing fast. If you're a creative and autonomous engineer with 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
- 8+ years of deep systems engineering experience with RPM-based Linux distributions
- Bachelor’s degree or equivalent work experience
- Strong experience automating multi-tier infrastructure configurations using Infrastructure as Code
- Experience with centralized infrastructure deployments and configuration management systems
- Deep understanding of virtualization and container internals, including Docker, Podman, Kubernetes, or MicroVMs
- Ability to design and build scalable container infrastructure, including registry management, custom image development, and layer optimization
- Proficiency in Python, Bash, or Go
- Deep operational understanding of OSI layers, TCP socket states, MTU optimization, packet captures, and modern networking
- Experience with more than one configuration management system
- Experience with CI/CD integration pipelines such as GitLab CI, GitHub Actions, or Jenkins
- Relevant industry certifications such as RHCE or CKA
- Experience managing GCP and AWS compute services
- Experience using LLMs to build and manage infrastructure
- Experience supporting EDA toolchains such as Synopsys or Cadence
- Experience with high-performance computing clusters and LSF or Slurm environments
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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