Senior Manager, Sales Engineering – AI, GPU Cloud

Mirantis

. Lead and build the sales engineering and solutions architecture team .

Posted 10/13/2026full-timeRemote • California • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading sales engineering and solutions architecture teams, with a strong focus on building discovery frameworks, architecting solutions across compute and networking, and driving POCs/POVs. Proficient in managing complex B2B deals and collaborating with technical and executive stakeholders to influence product roadmaps and capacity planning.

Highest-signal resume keywords

Sales Engineering LeadershipNVIDIA Compute Platforms ExpertiseGPU Architecture KnowledgeTCO/ROI Model DevelopmentMEDDPICC Methodology Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Distributed TrainingData PipelinesPyTorchCUDANVIDIA AI EnterpriseGPU OrchestrationTCO ComparisonsBenchmarkingML WorkloadsReference Architectures

Soft Skills

CoachingTeam BuildingCollaborationInfluencingCommunication

Tools & Technologies

NCCLInfiniBandKubernetesSlurmRun:aiTriton/TensorRT-LLMNGC EcosystemGrace-HopperDGXMGX

Industry Keywords

B2B SalesHPCCloud ComputingAI InfrastructureData Center Economics

Tech Stack

Tools & technologies

CloudKubernetesPyTorch

About the role

Key responsibilities & impact

Lead and build the sales engineering and solutions architecture team
Hire, coach, and retain sales engineers and solutions architects
Define the pre-sales operating model as the organization scales
Build discovery frameworks, reference architectures, TCO/benchmark models, POV playbooks, demo and benchmark environments, and RFP response libraries
Set and maintain the team's technical quality bar
Run enablement on GPU architecture, networking, and orchestration
Partner with Account Executives as technical lead on strategic and enterprise opportunities from discovery through technical close
Run qualification using MEDDPICC or an equivalent methodology
Architect solutions across compute, networking, storage, and orchestration
Produce sizing, capacity plans, and TCO comparisons against hyperscalers and self-build alternatives
Design and drive POCs/POVs, define success criteria, run benchmarks, and convert results into commercial momentum
Feed product and capacity requirements to product, platform, and supply/capacity planning teams
Work with NVIDIA field and partner ecosystem on reference architectures and joint pursuits
Influence product roadmap and packaging based on field insights

Requirements

What you’ll need

Hands-on experience running or standing up distributed training and/or production inference ML workloads
Practical fluency in data pipelines, distributed training, fine-tuning, and serving
Understanding of interconnect, memory bandwidth, I/O, and scheduling bottlenecks
Experience with PyTorch, NCCL, CUDA-level concepts, containers, and schedulers
Deep knowledge of NVIDIA compute platforms, including Hopper and Blackwell generations, H100/H200, GB200 NVL72, B200-class systems, Grace-Hopper, DGX, HGX, and MGX
Knowledge of NVLink/NVSwitch, InfiniBand, Spectrum-X Ethernet, RDMA/RoCE, and DPUs
Knowledge of NVIDIA AI Enterprise, NIM, NeMo, Triton/TensorRT-LLM, Base Command, Run:ai, GPU orchestration, and the NGC ecosystem
Understanding of the NVIDIA Cloud Partner motion and co-selling with NVIDIA
Track record supporting complex B2B deals with 6–18+ month cycles and large ACV/TCV
Experience navigating ML/infra, platform engineering, procurement, finance, security, and executive stakeholders
Ability to build and defend TCO/ROI models against hyperscaler and on-premises alternatives
Proven experience hiring, developing, and leading a sales engineering or solutions architecture team, or clearly demonstrated readiness to do so
Strongly preferred: experience selling GPU cloud, HPC, or specialized infrastructure
Preferred: Kubernetes, GPU operators/device plugins, Slurm, multi-cluster management, virtualized GPU/KubeVirt-style patterns, high-throughput parallel/object storage, data center economics, and sovereign/regulated/government AI buyers

Benefits

Comp & perks

Professional development and training
Conference and working group attendance
Company outings, happy hours, hackathons, and tech talks
Competitive compensation package with a strong benefits plan
Remote work availability