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Verified by our engine · Posted 4w ago
- Posted
- 4w ago
- Workplace
- Remote
- Salary
- Not disclosed
- Visa sponsorship
- Not specified
Posted on 9 September 2026
Work model: Remote
Salary range not shared by the company
Visa sponsorship details unknown
This role has closed. It's kept as a record — see NVIDIA's open roles or the similar live roles below.
See NVIDIA's open rolesNVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Within NVIDIA, the Networking Business Unit (NBU) builds the high-speed interconnect — Ethernet, InfiniBand, NVLink, and BlueField DPUs — that switches thousands of GPUs into a single AI supercomputer, moving data at the scale and speed the most demanding workloads require.
We are looking for a Technical Lead to join our Network System Validation group. You will work on validating advanced networking solutions across NVIDIA complex AI cluster environments. The group is a high-performance engineering force that treats validation as a first-class software problem. We build systems, frameworks, and benchmarks that prove our network's correctness and performance at scale. In this role you will lead the validation direction and engineering excellence of one of our technology validation teams. This is a deeply hands-on role for a technology leader who can own the technical roadmap, technology mentoring a team of high-performance engineers, and push NVIDIA's network to its speed-of-light limits. The role combines the development of validation methodologies and automation tools with hands-on debugging, performance analysis, and investigation of cutting-edge AI networking technologies at scale.
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
What we need to see:
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
Ways to stand out from the crowd:
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
We have some of the most forward-thinking and hardworking people working for us. If you're creative and autonomous, we want to hear from you!
About NVIDIA
NVIDIA's European sites — Munich, Zurich, Kyiv, Amsterdam — cover deep learning, driver and systems software rather than sales engineering. One of the few places in Europe doing serious GPU and inference infrastructure work.