Company Description Mirantis, an IREN company, is the Kubernetes-native AI infrastructure company, enabling organizations to build and operate scalable, secure, and sovereign infrastructure for modern AI, machine learning, and data-intensive applications. By combining open source innovation with deep expertise in Kubernetes orchestration, Mirantis empowers platform engineering teams to deliver composable, production-ready developer platforms across any environment—on-premises, in the cloud, at the edge, or in sovereign data centers. As enterprises navigate the growing complexity of AI-driven workloads, Mirantis delivers the automation, GPU orchestration, and policy-driven control needed to manage infrastructure with confidence and agility. Committed to open standards and freedom from lock-in, Mirantis ensures that customers retain full control of their infrastructure strategy. https://www.mirantis.com/ Job Description 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. Qualifications 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 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. Enterprise sales engineering on long, high-value cycles • 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. Proven team leadership • 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. 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 (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. • 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. Additional Information What does Mirantis offer you? • Work with an established Silicon Valley leader in the cloud infrastructure industry; • Work with exceptionally passionate, talented and engaging colleagues, helping Fortune 500 and Global 2000 customers implement next-generation cloud technologies; • Be a part of cutting-edge, open-source innovation; • Thrive in the high-energy environment of a young company where openness, collaboration, risk-taking, and continuous growth are valued; • Professional development and training; • Attend conferences and working groups; • Company outings, happy hours, hackathons, and tech talks; • Receive a competitive compensation package with a strong benefits plan. We are a Leader for Container Management in G2 (#2 after AWS)!