Senior Manager of Sales Engineering (AI / GPU Cloud, NeoCloud)
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Description
- 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
- 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)
- 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 layer: NVIDIA AI Enterprise, NIM, NeMo, Triton / TensorRT-LLM, Base Command, Run:ai / GPU orchestration, and the NGC ecosystem
- Understands the NVIDIA Cloud Partner motion and how to co-sell with NVIDIA
- Skilled at multi-stakeholder navigation — ML/infra leads, platform engineering, procurement, finance, security, and executive sponsors
- Can build and defend a TCO/ROI model against hyperscaler and on-prem alternatives, and translate performance benchmarks into commercial value
- Track record supporting complex B2B deals with cycles of 6–18+ months and large ACV/TCV, ideally including multi-year committed-capacity or reserved-capacity structures
- Has hired, developed, and led a sales engineering / solutions architecture team (or clearly demonstrated the readiness to), including building process and enablement from a light or greenfield starting point
- Player-coach mindset: still credible in the room on the hardest deals, while scaling others to do the same
- Experience selling GPU cloud, HPC, or specialized infrastructure — ideally at a NeoCloud / GPU-cloud provider, hyperscaler AI org, or accelerated-hardware vendor
- Hands-on with cloud-native and cluster orchestration for AI: Kubernetes (and GPU operators / device plugins), Slurm, and multi-cluster management approaches; familiarity with virtualized GPU / KubeVirt-style patterns is a plus
- Experience with data center economics and constraints: power, cooling, rack density, and how capacity availability shapes deals
- Storage-for-AI literacy — high-throughput parallel/object storage and its role in training pipelines
- Exposure to sovereign, regulated, or government AI buyers