Senior Manager of Sales Engineering (AI, GPU Cloud, NeoCloud)

About the role

Join our team as a Senior Manager of Sales Engineering, where you will lead and build the Sales Engineering/Solutions Architect team. You will be responsible for hiring, coaching, and retaining a team of sales engineers and solutions architects, defining the pre-sales operating model, and owning the technical win in large, complex deals. You will partner with Account Executives as the technical lead on strategic and enterprise opportunities, architect solutions across compute, networking, storage, and orchestration, and design and drive POCs/POVs. Additionally, you will be the technical voice of the customer internally and influence the roadmap and packaging based on field insights. Key missions: Lead and build the Sales Engineering / Solutions Architect team, including hiring, coaching, and retaining team members.. Define and implement the pre-sales operating model, including building reusable machinery such as discovery frameworks and reference architectures.. Partner with Account Executives as the technical lead on strategic and enterprise opportunities, running qualification with a real methodology. Profile: - 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) - You have actually run or stood up ML workloads — distributed training and/or production inference — not just talked about them - Comfortable in the frameworks and tooling customers use — PyTorch and the surrounding ecosystem (e.g., NCCL, CUDA-level concepts, containers, schedulers) - Software and platform layer: NVIDIA AI Enterprise, NIM, NeMo, Triton / TensorRT-LLM, Base Command, Run:ai / GPU orchestration, and the NGC ecosystem - 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 - Understands the NVIDIA Cloud Partner motion and how to co-sell with NVIDIA - 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 - 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 - 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 - 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 - Storage-for-AI literacy — high-throughput parallel/object storage and its role in training pipelines - Exposure to sovereign, regulated, or government AI buyers - Experience with data center economics and constraints: power, cooling, rack density, and how capacity availability shapes deals