Used Tools & Technologies
Not specified
Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Security @ 3
System Administration @ 4
Go @ 4
Kubernetes @ 4
Linux @ 4
Python @ 4
Algorithms @ 4
Data Structures @ 4
Distributed Systems @ 3
Machine Learning @ 4
Data Science @ 4
Hiring @ 4
Communication @ 3
Mathematics @ 4
Networking @ 3
GPU @ 4
Deep Learning @ 4
AI @ 4
InfiniBand @ 4
Slurm @ 4
HPC @ 4
NVLink @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
NVIDIA is hiring engineers to scale up its AI Infrastructure. We expect you to have a strong programming background, knowledge of datacenter hardware, operations, and networking, familiarity with software testing and deployment, familiarity with distributed systems, and excellent communication and planning abilities. Experience working with High Performance Computing (HPC), GPUs, and high-performance networking (RDMA, Infiniband, RoCE) are strongly preferred. We also welcome out-of-the-box thinkers who can provide new ideas with a strong execution bias. Expect to be constantly challenged, improving, and evolving for the better. You and other engineers on this team will help advance NVIDIA's capacity to build and deploy leading infrastructure solutions for a broad range of AI-based applications that affect core data science.
For two decades, we have pioneered visual computing, the art and science of computer graphics. With the invention of the GPU - the engine of modern visual computing - the field has expanded to encompass video games, movie production, product design, medical diagnosis and scientific research. Today, we stand at the beginning of the next era, the AI computing era, ignited by a new computing model, GPU deep learning.
Responsibilities
- Contribute to a comprehensive platform that automates GPU asset provisioning, configuration, and lifecycle management across cloud providers; build end-to-end automation of datacenter operations, break/fix, and lifecycle management for large-scale Machine Learning systems.
- Implement monitoring and health management capabilities to enable reliability, availability, and scalability of GPU assets; harness multiple data streams ranging from GPU hardware diagnostics to cluster and network telemetry.
- Work on software that manages NVLINK topography across GPU clusters.
- Build automated test infrastructure used to qualify distributed systems for operation.
- Work with engineering teams across NVIDIA to ensure software integrates from hardware up to AI training applications.
- Continuously innovate and discover new problems and solutions.
Requirements
- Highly motivated with strong communication skills; ability to work successfully with cross-functional teams, principals and architects, and coordinate across organizational boundaries and geographies.
- 10+ years of software engineering experience on large-scale production systems.
- BS in Computer Science/Engineering/Physics/Mathematics or comparable degree or equivalent experience.
- Expert level knowledge of a systems programming language (Go, Python) and a solid understanding of Data Structures and Algorithms.
- Expert level knowledge of Linux system administration and management.
- Understanding of cluster management systems (Kubernetes, 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 / Preferred
- Proficiency in architecting and managing large-scale distributed systems, independent of cloud providers.
- Deep knowledge of datacenter operations and GPU hardware; hands-on experience working with RDMA networking (Infiniband, RoCE).
- Advanced hands-on experience and deep understanding of cluster management systems (Kubernetes, SLURM); hands-on experience in Machine Learning Operations and Bright Cluster Manager.
- Hands-on experience developing and/or operating hardware fleet management systems; proven operational excellence in designing and maintaining AI infrastructure.
Compensation & Benefits
- Base salary range (by level/location): Level 4: 184,000 USD - 287,500 USD; Level 5: 224,000 USD - 356,500 USD.
- You will also be eligible for equity and benefits (link to NVIDIA benefits page in original posting).
Additional information
- Applications for this job will be accepted at least until July 11, 2026.
- This posting is for an existing vacancy.
- NVIDIA uses AI tools in its recruiting processes.
- NVIDIA is an equal opportunity employer and committed to fostering an inclusive work environment.