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Mirantis is seeking a Senior Manager of Sales Engineering to lead the technical sales function for K0rdent AI, an orchestration layer that transforms disaggregated GPU infrastructure into a multi-tenant, production-ready AI cloud. The company sells 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 (6–18+ months) where credibility and technical architecture are decisive.
You will own the technical win across the sales process, building and leading the sales engineering and solutions architecture team as the organization scales. Your responsibilities include:
**Team Leadership & Operations:**
- Hire, coach, and retain a team of sales engineers and solutions architects; define the pre-sales operating model.
- Build reusable machinery: discovery frameworks, reference architectures, TCO/benchmark models, POV playbooks, demo environments, and RFP response libraries.
- Set and enforce a technical quality bar; run enablement so every SE can credibly discuss GPU architecture, networking, and orchestration.
**Technical Deal Ownership:**
- Partner with Account Executives as the technical lead on strategic and enterprise opportunities from discovery through close.
- Run qualification using structured methodology (MEDDPICC or equivalent); surface economic buyers, decision criteria, and technical champions.
- Architect solutions across compute, networking, storage, and orchestration; produce sizing, capacity plans, and TCO comparisons vs. hyperscalers and self-build alternatives.
- Design and drive POCs/POVs: define success criteria, run benchmarks, and convert results into commercial momentum.
**Customer & Product Voice:**
- Feed structured product and capacity requirements back to product, platform, and supply/capacity planning teams.
- Work with NVIDIA field and partner ecosystem (Cloud Partner program, reference architectures, joint pursuits) to strengthen deals.
- Influence roadmap and packaging based on field learnings.
This is not a demo-jockey role. You must have genuinely stood up training and inference workloads, argued interconnect topology with customers' ML infrastructure leads, and closed large deals with long sales cycles.
**Requirements:**
*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 discussed 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 live (interconnect, memory bandwidth, I/O, scheduling).
- Comfortable with frameworks and tooling customers use—PyTorch and surrounding ecosystem (NCCL, CUDA-level concepts, containers, schedulers).
- Deep knowledge of the NVIDIA platform and GPU products: current on NVIDIA compute stack across Hopper and Blackwell generations (H100/H200, GB200 NVL72, B200-class systems, Grace-Hopper superchips) and reference-system families (DGX, HGX, MGX).
- 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 knowledge: NVIDIA AI Enterprise, NIM, NeMo, Triton/TensorRT-LLM, Base Command, Run:ai/GPU orchestration, and NGC ecosystem.
- Understands the NVIDIA Cloud Partner motion and co-sell approach.
- Track record supporting complex B2B deals with 6–18+ month cycles 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 TCO/ROI models against hyperscaler and on-prem alternatives; translate performance benchmarks into commercial value.
- Proven team leadership: has hired, developed, and led a sales engineering/solutions architecture team (or clearly demonstrated readiness), including building process and enablement from light or greenfield starting point.
- Player-coach mindset: still credible on the hardest deals while scaling others.
*Strongly Preferred:*
- 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 (GPU operators/device plugins), Slurm, multi-cluster management; familiarity with virtualized GPU/KubeVirt-style patterns.
- Storage-for-AI literacy—high-throughput parallel/object storage and its role in training pipelines.
- Experience with data center economics and constraints: power, cooling, rack density, and how capacity availability shapes deals.
- Exposure to sovereign, regulated, or government AI buyers.
