携程Senior AI Solution EN SHACC FLT(MJ036070)

We are looking for a networking test engineer with strong system‑level debugging skills to join our End‑to‑End Verification team. You will work on cutting‑edge Ethernet‑based AI clusters, owning complex issues across hardware, system software and AI workloads.  What you’ll be doing: • Design and review test and product requirements across the Ethernet / NIC / DPU / Switch portfolio, focusing on large‑scale AI cluster behavior. • Build and maintain realistic customer‑like testbeds, including heterogeneous hardware, OS / driver combinations and complex network fabrics. • Own end‑to‑end cluster troubleshooting: reproduce customer scenarios, triage across the stack and drive issues to root cause and fix. • Read and understand relevant source code to identify defects, validate fixes and improve logging and instrumentation. • Collaborate closely with development teams to debug NCCL, RoCE/RDMA and related networking components using logs, code inspection and targeted experiments. • Define tests and guide the automation team to implement robust suites that produce actionable logs, metrics and traces. • Run Regression, Performance, Functional and Scale testing, analyze results and provide clear, data‑driven reports to stakeholders. • Profile and benchmark deep learning training and inference workloads, correlating model‑level metrics with system and network telemetry to uncover bottlenecks.

• Lead technical exploration with customer architects to understand models, frameworks, SLOs, and KV cache usage patterns. • Build end-to-end KV cache solutions using tiered memory and NVIDIA modern networking technologies. • Analyze performance profiles, identify bottlenecks, and drive PoCs and benchmarks to validate improvements. • Translate customer difficulties into clear feature requests and roadmap input for NVIDIA products. • Build reference architectures, best-practice guides, and deliver tech talks to support our field teams and customers.

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Define AI product vision and strategy across Global Selling seller segments and lifecycle stages; identify high-impact opportunities to apply AI/ML (e.g., GenAI, agentic AI, intelligent automation) to empower business teams — including Marketing, CRM, seller acquisition, and seller growth — to drive operational efficiency and business outcomes at scale. Own the end-to-end AI product lifecycle — from ideation and opportunity sizing, through technical development and experimentation, to launch, adoption, and iterative improvement — ensuring alignment between business objectives, user needs, and technological capabilities. Develop and communicate product requirements including model objectives, data specifications, evaluation metrics, and responsible AI guardrails; earn trust from cross-functional stakeholders (engineering, applied science, design, business operations) and external partners through clear documentation and aligned decision-making. Lead cross-functional and cross-regional collaboration with scientists, engineering, and data teams to build, evaluate, and ship AI-powered products; drive development velocity by managing trade-offs, unblocking technical dependencies, resolving issues, and maintaining rigorous experimentation standards (A/B testing, model performance benchmarks). Drive measurable business impact post-launch through continuous monitoring, data-driven iteration, and feedback loops; leverage market analysis and competitive intelligence to inform product positioning and roadmap evolution. Manage stakeholder and leadership communications across multiple geographies — including problem framing, AI solution trade-off analysis, product/business reviews, budget and headcount planning, and organizational change management for AI adoption at scale. Partner with international stakeholders, leaders, and tech teams to align priorities and drive execution across regions.