Senior Manager, Sales Engineering — AI / GPU Cloud (NeoCloud)

Why this role exists K0rdent AI is the orchestration layer that turns raw, disaggregated GPU infrastructure into a multi-tenant, production-ready AI cloud — without locking companies into a single hyperscaler or hardware vendor. We sell accelerated compute: GPU clusters, bare metal, and managed AI infrastructure to Neoclouds, AI-native startups, enterprise AI teams, research labs, and sovereign/regulated buyers. These are technical, high-value, long-cycle deals where the sale is won or lost on credibility: whether we can architect the right cluster, model the real TCO, prove performance, and de-risk a customer's move onto our platform. This person owns the technical win. They build and lead the sales engineering function that turns "interested" into signed, multi-year committed-capacity contracts, and they set the pre-sales bar as we scale headcount and deal volume. This is not a demo-jockey role. We need someone who has genuinely stood up training and inference workloads, argued interconnect topology with a customer's ML infra lead, and closed large deals with cycles measured in quarters, not weeks. What you'll own Lead and build the SE / Solutions Architect team • Hire, coach, and retain a team of sales engineers and solutions architects; define the pre-sales operating model as the org scales. • Build the reusable machinery: discovery frameworks, reference architectures, TCO/benchmark models, POV playbooks, demo and benchmark environments, RFP response libraries. • Set and hold a technical quality bar across the team; run enablement so every SE can speak credibly to GPU architecture, networking, and orchestration. Own the technical win in large, complex deals • Partner with Account Executives as the technical lead on strategic and enterprise opportunities from discovery through technical close. • Run qualification with a real methodology (MEDDPICC or equivalent) — surface the economic buyer, decision criteria, and the technical champion, and build the win plan around them. • Architect solutions across compute, networking, storage, and orchestration; produce sizing, capacity plans, and TCO comparisons vs. hyperscalers and self-build. • Design and drive POCs/POVs: define success criteria up front, run benchmarks, and convert results into commercial momentum. Be the Technical voice of the Customer internally • Feed structured product and capacity requirements back to product, platform, and supply/capacity planning. • Work alongside the NVIDIA field and partner ecosystem (Cloud Partner program, reference architectures, joint pursuits) to strengthen deals. • Influence roadmap and packaging based on what you learn in the field.   Required: Real, hands-on AI/ML infrastructure experience • You have actually run or stood up ML workloads — distributed training and/or production inference — not just talked about them. • Practical fluency in the training and inference lifecycle: data pipelines, distributed training (multi-node/multi-GPU), fine-tuning, and serving; you understand where bottlenecks actually live (interconnect, memory bandwidth, I/O, scheduling). • Comfortable in the frameworks and tooling customers use — PyTorch and the surrounding ecosystem (e.g., NCCL, CUDA-level concepts, containers, schedulers). Deep knowledge of the NVIDIA platform and GPU products • Current on the NVIDIA compute stack across the Hopper and Blackwell generations (e.g., H100/H200, GB200 NVL72 / B200-class systems, Grace-Hopper superchips) and the reference-system families (DGX, HGX, MGX); aware of what's coming next-generation. • Networking fluency: NVLink/NVSwitch domains, InfiniBand (Quantum) vs. Spectrum-X Ethernet fabrics, RDMA/RoCE, DPUs — and why fabric choice makes or breaks large training clusters. • Software and platform laye