Note: The job is a remote job and is open to candidates in USA. NVIDIA is hiring engineers to scale up its AI Infrastructure. The role involves contributing to the automation of datacenter operations and ensuring the reliability and scalability of GPU assets for AI-based applications.
Responsibilities
- You will contribute to this platform to 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 that enable industry-leading reliability, availability, and scalability of GPU assets
- You will be harnessing 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 that we use to qualify distributed systems for operation
- Work with engineering teams across NVIDIA to ensure your software integrates seamlessly from the hardware all the way up to the AI training applications
- You'll be constantly innovating, discovering new problems and their solutions
Skills
- Highly motivated with strong communication skills
- Ability to work successfully with multi-functional teams, principles and architects and coordinate effectively across organizational boundaries and geographies
- 10+ years of software engineering experience on large-scale production systems
- Possess a 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 a solid understanding of Data Structure 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
- Experience working with High Performance Computing (HPC), GPUs, and high-performance networking (RDMA, Infiniband, RoCE) are strongly 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
- Advanced hands-on experience and deep understanding of cluster management systems (Kubernetes, SLURM)
- Hands-on experience in Machine Learning Operations
- Hands-on experience with Bright Cluster Manager
- Hands-on experience developing and/or operating hardware fleet management systems
- Proven operational excellence in designing and maintaining AI infrastructure
Benefits
- You will also be eligible for equity and [benefits](https://www.nvidia.com/en-us/benefits/).
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