SENIOR MANAGER, SALES ENGINEERING — AI / GPU CLOUD (NEOCLOUD)

Mirantis

Full-timesenior

Job description

Why this role existsK0rdent 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 ownLead and build the SE / Solutions Architect team<ul><li>Hire, coach, and retain a team of sales engineers and solutions architects; define the pre-sales operating model as the org scales.</li><li>Build the reusable machinery: discovery frameworks, reference architectures, TCO/benchmark models, POV playbooks, demo and benchmark environments, RFP response libraries.</li><li>Set and hold a technical quality bar across the team; run enablement so every SE can speak credibly to GPU architecture, networking, and orchestration.</li></ul>Own the technical win in large, complex deals<ul><li>Partner with Account Executives as the technical lead on strategic and enterprise opportunities from discovery through technical close.</li><li>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.</li><li>Architect solutions across compute, networking, storage, and orchestration; produce sizing, capacity plans, and TCO comparisons vs. hyperscalers and self-build.</li><li>Design and drive POCs/POVs: define success criteria up front, run benchmarks, and convert results into commercial momentum.</li></ul>Be the Technical voice of the Customer internally<ul><li>Feed structured product and capacity requirements back to product, platform, and supply/capacity planning.</li><li>Work alongside the NVIDIA field and partner ecosystem (Cloud Partner program, reference architectures, joint pursuits) to strengthen deals.</li><li>Influence roadmap and packaging based on what you learn in the field.</li></ul>  Required:Real, hands-on AI/ML infrastructure experience<ul><li>You have actually run or stood up ML workloads — distributed training and/or production inference — not just talked about them.</li><li>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).</li><li>Comfortable in the frameworks and tooling customers use — PyTorch and the surrounding ecosystem (e.g., NCCL, CUDA-level concepts, containers, schedulers).</li></ul>Deep knowledge of the NVIDIA platform and GPU products<ul><li>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.</li><li>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.</li><li>Software and platform layer: NVIDIA AI Enterprise, NIM, NeMo, Triton / TensorRT-LLM, Base Command, Run:ai / GPU orchestration, and the NGC ecosystem.</li><li>Understands the NVIDIA Cloud Partner motion and how to co-sell with NVIDIA.</li></ul>Enterprise sales engineering on long, high-value cycles<ul><li>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.</li><li>Skilled at multi-stakeholder navigation — ML/infra leads, platform engineering, procurement, finance, security, and executive sponsors.</li><li>Can build and defend a TCO/ROI model against hyperscaler and on-prem alternatives, and translate performance benchmarks into commercial value.</li></ul>Proven team leadership<ul><li>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.</li><li>Player-coach mindset: still credible in the room on the hardest deals, while scaling others to do the same.</li></ul>Strongly preferred<ul><li>Experience selling GPU cloud, HPC, or specialized infrastructure — ideally at a NeoCloud / GPU-cloud provider, hyperscaler AI org, or accelerated-hardware vendor.</li><li>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.</li><li>Storage-for-AI literacy — high-throughput parallel/object storage and its role in training pipelines.</li><li>Experience with data center economics and constraints: power, cooling, rack density, and how capacity availability shapes deals.</li><li>Exposure to sovereign, regulated, or government AI buyers.</li></ul> What does Mirantis offer you?<ul><li>Work with an established Silicon Valley leader in the cloud infrastructure industry;</li><li>Work with exceptionally passionate, talented and engaging colleagues, helping Fortune 500 and Global 2000 customers implement next-generation cloud technologies;</li><li>Be a part of cutting-edge, open-source innovation;</li><li>Thrive in the high-energy environment of a young company where openness, collaboration, risk-taking, and continuous growth are valued;</li><li>Professional development and training;</li><li>Attend conferences and working groups;</li><li>Company outings, happy hours, hackathons, and tech talks;</li><li>Receive a competitive compensation package with a strong benefits plan.</li></ul><div sr-tagline=""></div>We are a <a target="_blank" href="https://www.g2.com/reports/grid-report-for-container-management-spring-2022.embed?featured=mirantis-kubernetes-engine-formerly-docker-enterprise&secure%5Bgated_consumer%5D=7ed17484-74e3-4ce8-8ad6-b48b395fbf56&secure%5Btoken%5D=f4b909a5c1a2d1aa71dee93761486db5732a5b82abd47aa75f3353da41e3b92c&utm_campaign=gate-817340" rel="noopener noreferrer">Leader for Container Management</a> in G2 (#2 after AWS)!