Senior Manager, Sales Engineering — AI / GPU Cloud (NeoCloud)

Mirantis, Inc.; San Jose (CA); On-site; USD 260,000 - 460,000; Full time

Benefits offered by this job

Competitive compensation

Advanced tech environment

Conferences & training

Company outings & events

Strong benefits plan

Job summary

Mirantis, Inc. in Silicon Valley seeks a lead Sales Engineer/Solutions Architect to own the technical win in large, complex AI infrastructure deals.

You will build and lead a team of SEs and architects, define the pre-sales operating model, and craft TCO/benchmark materials to win multi-year commitments. You will partner with account executives, run MEDDPICC-style qualification, design solutions across compute, networking, storage, and orchestration, and translate benchmarks into commercial

Qualifications

• Run or stand up ML workloads—distributed training or production inference.
• Fluency with training/inference lifecycles, data pipelines, and bottlenecks.
• Comfort with PyTorch, CUDA concepts, containers, schedulers.

Responsibilities

• Lead and build the SE/solutions architect team for pre-sales scale.
• Own the technical win on enterprise opportunities from discovery to close.
• Architect solutions across compute, networking, storage, and orchestration.

Skills

AI/ML infra

Distributed training

Inference workloads

PyTorch ecosystem

NVIDIA GPUs

Tools

NVIDIA software stack

Job 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.

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