What you will be doing:

• Join a core group of engineers with high critical-thinking abilities passionate about tackling some of the most sophisticated and hard problems in distributed systems and fault-tolerant design in real-world production systems
• Solid technical foundation in distributed computing and storage, including significant experience with most of the following: server systems, operating systems, storage, I/O, networking, and system software.
• Design, develop, test, and maintain cluster monitoring and validation systems.
• Expand and optimize container orchestration infrastructure (K8s) for AI model training and inference, high-performance server systems, storage, I/O, networking, and system software.
• Deploy, monitor, and debug your software in production environments
• Work with engineering teams across all of NVIDIA to ensure your software integrates seamlessly up and down the stack.

What we need to see:

• Deep understanding of data structures, concurrency, fault-tolerance, scalable runtime systems, operating systems and distributed systems design.
• Strong programming skills and expert-level knowledge of a systems programming language (C/C++/Go).
• Highly motivated with strong interpersonal skills, you have the ability to work successfully with multi-functional teams, principals, and architects and coordinate effectively across interpersonal boundaries and geographies.
• 5+ years of software engineering or research lab experience on large-scale systems
• Ph.D/MS/BS in Computer Science/Engineering/Physics/Mathematics or other comparable Degree or equivalent experience
• Solid understanding of performance, security, and reliability in complex distributed systems. Familiarity with system-level architecture, such as interconnects, memory hierarchy, interrupts, and memory-mapped IO

Ways to stand out from the crowd:

• Hands-on development experience with OS internals, schedulers, networking, and container runtimes, and scale-out systems design.
• Familiarity with AI/ML technologies
• Background with batch scheduling with K8s, Mesos, Slurm
• Operational experience in AI Infrastructure and large-scale distributed systems