1 day ago
Bengaluru, IndiaSenior
Responsibilities
- Design, build, deploy, and operate internal tooling for large-scale AI training and inference platforms.
- Conduct performance characterization and analysis on large multi-GPU and multi-node clusters.
- Manage services across their full lifecycle, from system design and deployment through operation and refinement.
- Support launches through system design consulting, software and platform development, capacity management, and launch reviews.
- Monitor availability, latency, and overall system health of production services.
- Scale systems through automation and improve reliability and engineering velocity.
- Participate in incident response, blameless postmortems, and an on-call rotation.
Requirements
- Bachelor’s degree in Computer Science or a related coding-oriented technical field, or equivalent experience.
- At least 5 years of experience.
- Experience with infrastructure automation and distributed systems design for large-scale private or public cloud systems in production.
- Programming experience in one or more of Python, Go, C, C++, or Java.
- In-depth knowledge of one or more of Linux, networking, storage, or container technologies.
- Experience with public cloud, infrastructure as code, and Terraform.
- Distributed systems experience.
- Preferred experience running large private or public cloud systems based on Kubernetes or Slurm.
- Strong problem-solving, communication, ownership, collaboration, debugging, optimization, and automation abilities.
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.