Senior E2E Hardware Architect, AI Networking
NVIDIA is hiring a Senior E2E Hardware Architect, AI Networking. Level rates it ; you can apply on NVIDIA.
Level's read on this job
- Level rates this role Little AI ●○○○: aI is not part of the work.
- Level classifies it as a senior role.
- Level lists 2,600+ other open roles at NVIDIA.
- The role was posted in the last few days.
AI in this role
NVIDIA is excited to invite an experienced and innovative Senior E2E Hardware Architect to join our Networking Architecture team in Israel. This role offers an outstanding chance to develop the next generation of networking software and hardware that powers AI data centers and accelerated computing. You will lead end-to-end architectural explorations, find opportunities for innovation, and introduce new networking features and technologies into future NVIDIA platforms.
What You’ll Be Doing
- Lead end-to-end architecture explorations across host, NIC, and switch hardware to define innovative networking technologies and capabilities.
- Identify architectural bottlenecks, technology gaps, and optimization opportunities across networking systems.
- Define and drive new hardware features and architectural improvements for next-generation NVIDIA networking platforms.
- Explore architectural alternatives and evaluate tradeoffs involving performance, resiliency, efficiency, scalability, complexity, and hardware resources.
- Translate system-level requirements and emerging workloads into concrete hardware architecture proposals and feature definitions.
- Drive architectural initiatives from early research and proof of concept through feasibility analysis, specification, and handoff to product architecture and development teams.
- Collaborate with hardware, firmware, software, and performance modeling teams to evaluate and validate architectural concepts.
- Examine networking issues in extensive data centers and accelerated computing settings.
What We Need To See
- B.Sc. or M.Sc. in Electrical Engineering, Computer Engineering, Computer Science, or equivalent experience.
- 8+ years of industry experience in hardware architecture, microarchitecture, or system architecture.
- Proven hardware architecture experience in at least one of the following:
- NICs or DPUs.
- High-performance Ethernet switches or networking ASICs.
- Host architecture, CPU/SoC subsystems, or high-speed I/O and interconnect architecture.
- Strong understanding of hardware architecture principles, performance optimization, and architectural tradeoffs.
- Demonstrated ability to analyze complex technical problems, explore alternative solutions, and define innovative architectural approaches.
- Strong system-level thinking and the ability to understand interactions between hardware components and software.
- Experience driving architectural features or technical initiatives across multidisciplinary engineering teams.
- Excellent communication, analytical, and technical leadership skills.
Ways To Stand Out From The Crowd
- Knowledge of Ethernet networking, RDMA/RoCE, InfiniBand, or advanced networking protocols.
- Experience with large-scale data center networks, high-performance computing, or distributed systems.
- Experience with end-to-end performance analysis, architecture modeling, or simulation.
- Understanding of congestion control, flow control, packet processing, or network transport mechanisms.
- Experience defining new hardware capabilities across multiple components or platforms.
Join NVIDIA and play a role in developing the networking technologies that will support the next generation of AI infrastructure!
How we rate this
Senior E2E Hardware Architect, AI Networking at NVIDIA rates 28 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.
Little AI. AI is not part of the work.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
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