Requirements:

• B.Sc. / B.A. in Computer Science, Electrical Engineering, or equivalent experience
• 8+ years of experience in networking, system validation, or related domains
• Proven experience debugging complex production systems by forming hypotheses, designing experiments, and driving issues to root cause
• Ability to read, debug, and reason about C/C++ code (Rust or Go a plus)
• Strong scripting and automation experience using Python, Bash, and/or Ansible
• Deep understanding of distributed systems: concurrency, consistency models, fault tolerance, and large-scale system performance under stress
• Ability to drive technical alignment across teams, communicate tradeoffs clearly, and make high-quality architectural decisions at speed
• Advance AI-driven approaches to test automation: intelligent scenario generation, LLM-augmented root-cause analysis, and autonomous validation pipelines

Nice to Haves:

• Experience with large-scale clusters or distributed systems
• Familiarity with NVIDIA networking solutions (ConnectX, SpecX, BlueField)
• Background in performance analysis, Kubernetes, or cloud environments
• Background in chaos testing, fault injection, or simulation systems

What you'll be doing:

• Review system and product requirements, design validation methodologies, develop and implement comprehensive test plans, functional and performance, for networking technologies in large-scale AI cluster solutions
• Develop and maintain benchmarks, automation tools and scripts for test execution, environment setup, log collection, and data analysis
• Lead end-to-end investigation of complex issues by reproducing real-world scenarios, analyzing logs, telemetry, packet captures, and system metrics to identify functional issues and performance bottlenecks, triaging problems across the hardware and software stack, and driving them to root cause and resolution
• Read and understand source code (C/C++/Python) to investigate defects, validate fixes, and improve logging, instrumentation, and debugging capabilities
• Collaborate deeply with software and hardware development teams to debug networking technologies, including NCCL, RoCE, RDMA, and related software components using targeted experiments and code inspection
• Profile and research AI training and inference workloads, correlating application behavior with network and system telemetry to identify scalability and performance limitations
• Document findings, communicate technical results, and continuously improve validation methodologies, automation environments, and engineering processes

Perks and Benefits:

• Joining a high-performance engineering force in the Networking Business Unit
• Opportunity to work with cutting-edge AI networking technologies at scale
• Contribute to the technical roadmap and mentor a team of high-performance engineers
• Possibility to push NVIDIA's network to its speed-of-light limits
• Continuous learning and development in the field of networking and AI