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

Must-have qualifications:

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.

What good looks like

First 90 days: deep on our platform and differentiators; embedded as technical lead on the top active opportunities; a clear read on the current team, gaps, and the pre-sales process to fix first.

6 months: a repeatable POV and TCO framework in use across the team; measurable improvement in technical-win rate and POC-to-close conversion; a hiring plan (or hires) closing the biggest coverage gaps.

12 months: a scaled, high-credibility SE org that AEs actively pull into strategic deals; SE involvement correlated with larger deal size, faster technical close, and higher win rate on the deals that matter most.

Compensation & logistics

Structure: competitive base + variable tied to team bookings/attainment, plus equity.

• Indicative OTE: senior people-leader band for AI-infra pre-sales, strong candidates in this space command a premium.
• Location / travel: remote or. hub-based, expect meaningful travel to customers, data centers, and NVIDIA/partner events.