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

Mirantis; San Jose (CA); On-site; USD 300,000 - 500,000; Full time

Job summary

Mirantis, Inc. in San Jose seeks an accomplished Leader to build and run the SE/Solutions Architect team for complex enterprise AI infrastructure deals. You will hire, coach, and scale the pre-sales function while owning technical wins and shaping reference architectures and TCO models.

You will partner with Account Executives, drive MEDDPICC discovery-to-close, and translate field insights into product and capacity planning. This role demands deep GPU/AI, orchestration, and cloud experience.

Qualifications

• Extensive track record leading SE/SA teams in complex B2B AI infra deals.
• Proven ability to build scalable pre-sales processes and RO/CO alignment.
• Strong expertise in GPU orchestration, networking, and storage for AI workloads.

Responsibilities

• Lead and build the SE / Solutions Architect team.
• Hire, coach, and retain a team of SEs and SAs; define scalable pre-sales operating model.
• Build reusable discovery frameworks, reference architectures, TCO/benchmark models, POV playbooks, demo environments, RFP libraries.
• Set and hold a technical quality bar across the team; enable GPU, networking, orchestration.
• Own the technical win in large, complex deals.
• Partner with Account Executives as the technical lead from discovery to close.
• Run MEDDPICC-style qualification to surface buyers, criteria, and champions.
• Architect solutions across compute, networking, storage, and orchestration; provide sizing and TCO comparisons.
• Design and drive POCs/POVs; define success criteria and convert results to momentum.
• Be the technical voice of the customer internally.
• Feed product and capacity requirements back to product and planning.
• Collaborate with NVIDIA field and partners to strengthen deals.
• Influence roadmap and packaging based on field learnings.

Skills

Leadership

Sales engineering

Strategic deals

Stakeholder management

GPU/AI infra

TCO modeling

Cloud orchestration

Kubernetes

POC design

Negotiation

Tools

Kubernetes

TensorRT

CUDA

NVIDIA AI Enterprise

Slurm

KubeVirt

Run:AI

Job description

• 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

You have actually run or stood up ML workloads — distributed training and/or production inference — not just talked about themPractical 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)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-generationNetworking fluency: NVLink/NVSwitch domains, InfiniBand (Quantum) vs. Spectrum-X Ethernet fabrics, RDMA/RoCE, DPUs — and why fabric choice makes or breaks large training clustersSoftware and platform layer: NVIDIA AI Enterprise, NIM, NeMo, Triton / TensorRT-LLM, Base Command, Run:ai / GPU orchestration, and the NGC ecosystemUnderstands the NVIDIA Cloud Partner motion and how to co-sell with NVIDIASkilled at multi-stakeholder navigation — ML/infra leads, platform engineering, procurement, finance, security, and executive sponsorsCan build and defend a TCO/ROI model against hyperscaler and on-prem alternatives, and translate performance benchmarks into commercial valueTrack record supporting complex B2B deals with cycles of 6–18+ months and large ACV/TCV, ideally including multi-year committed-capacity or reserved-capacity structuresHas 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 pointPlayer-coach mindset: still credible in the room on the hardest deals, while scaling others to do the sameExperience selling GPU cloud, HPC, or specialized infrastructure — ideally at a NeoCloud / GPU-cloud provider, hyperscaler AI org, or accelerated-hardware vendorHands-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 plusExperience with data center economics and constraints: power, cooling, rack density, and how capacity availability shapes dealsStorage-for-AI literacy — high-throughput parallel/object storage and its role in training pipelinesExposure to sovereign, regulated, or government AI buyers