Additionalinformation

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)!

Companydescription

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/

Jobdescription

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.