4 days ago
Remote, Canada or Toronto, CanadaSenior
Responsibilities
- Build automation for GPU asset provisioning, configuration, lifecycle management, datacenter management, break/fix, and system upkeep across cloud providers.
- Implement monitoring and health-management capabilities for GPU asset reliability, availability, and scalability.
- Process GPU hardware diagnostics, cluster telemetry, and network telemetry.
- Build automated test infrastructure to qualify distributed systems for operation.
- Partner with engineering teams to integrate software from hardware through AI training applications.
Requirements
- At least 5 years of experience in a similar role working on large-scale production systems.
- Bachelor’s degree in Computer Science, Engineering, Physics, Mathematics, or a comparable field, or equivalent experience.
- Expert knowledge of at least one systems programming language, including Go, Python, and/or Rust.
- Strong understanding of data structures and algorithms, Linux system administration, container orchestration, system architecture, data synchronization, fault tolerance, and state management.
- Understanding of performance, security, and reliability in complex distributed systems.
- Preferred experience designing widespread distributed networks, managing large-scale distributed systems independently of cloud providers, datacenter operations, GPU hardware, RDMA networking, Kubernetes, and SLURM.
Benefits
- Eligible for equity and benefits.
- Applications will be accepted at least until November 6, 2026.
- This posting is for an existing vacancy.
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.