Join a team of engineers at NVIDIA dedicated to scaling the AI infrastructure that powers cutting-edge data science applications. Our team solves complex challenges across datacenter hardware, networking, distributed systems, and software deployment. We collaborate closely to design and deliver leading infrastructure solutions. We value strong technical foundations paired with creative thinking and a bias toward execution. Together, we continuously push boundaries, grow our skills, and advance NVIDIA’s capacity to build and deploy AI-based infrastructure at scale.
What you will be doing:
- Contribute to a comprehensive platform that automates GPU asset provisioning, configuration, and lifecycle management across cloud providers.
- Build end-to-end automation of data center management, break/fix, and the full span of system upkeep for large-scale Machine Learning systems.
- Implement monitoring and health management capabilities that enable industry-leading reliability, availability, and scalability of GPU assets.
- Harness multiple data streams, ranging from GPU hardware diagnostics to cluster and network telemetry.
- Build automated test infrastructure that we use to qualify distributed systems for operation.
- Partner with engineering teams across NVIDIA to ensure the software integrates seamlessly from the hardware all the way up to the AI training applications.
What we need to see:
- 12+ years of experience in a similar role working on large-scale production systems.
- BS in Computer Science/Engineering/Physics/Mathematics or other comparable degree, or equivalent experience.
- Expert level knowledge of a systems programming language (Go, Python, and/or Rust) and a solid understanding of Data Structures and Algorithms.
- Strong background in Linux system administration and management.
- Deep knowledge of container orchestration platforms such as Kubernetes and/or SLURM.
- Understanding of performance, security, and reliability in complex distributed systems.
- Familiarity with system level architecture, data synchronization, fault tolerance, and state management.
Ways to stand out from the crowd:
- Proficiency in designing and building widespread distributed networks.
- Experience managing large-scale distributed systems independently of cloud providers.
- Extensive expertise in datacenter operations and GPU hardware.
- Hands-on experience working with RDMA networking.
- Advanced hands-on experience and deep understanding of cluster management systems (Kubernetes, SLURM).
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
- 12+ years of experience in a similar role working on large-scale production systems
- Bachelor’s degree in Computer Science, Computer Engineering, Physics, Mathematics, or a comparable degree, or equivalent experience
- Expert-level knowledge of a systems programming language such as Go, Python, and/or Rust
- Strong understanding of data structures and algorithms
- Strong background in Linux system administration and management
- Deep knowledge of container orchestration platforms such as Kubernetes and/or SLURM
- Understanding of performance, security, and reliability in complex distributed systems
- Familiarity with system-level architecture, data synchronization, fault tolerance, and state management
- Experience designing and building widespread distributed networks
- Experience managing large-scale distributed systems independently of cloud providers
- Extensive expertise in data center operations and GPU hardware
- Hands-on experience with RDMA networking
- Advanced hands-on experience and deep understanding of Kubernetes or SLURM cluster management systems
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.
-
Equity Value & Accessibility — Company stock and RSUs have driven exceptional total compensation, with a generous employee stock purchase plan and ongoing equity refreshers playing a central role. Feedback suggests historic share-price gains created significant financial upside for many.
-
Healthcare Strength — Multiple plan options (HSA, PPO, and in some locations Kaiser) with in-network preventive care covered at 100% and company HSA contributions indicate a robust offering. Mental-health sessions and broad dental, vision, and disability coverage further strengthen the package.
-
Parental & Family Support — Up to 22 weeks of fully paid parental leave, adoption and surrogacy reimbursement, and coverage for IVF and egg freezing are highlighted as standout features. Additional family-care resources and caregiving support broaden the appeal.
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.”
