Senior Manager, Sales Engineering – AI / GPU Cloud

Mirantis

Sales engineering leader building Mirantis’s GPU-cloud pre-sales organization for AI infrastructure. Owning technical wins, architectures, benchmarks, and enterprise committed-capacity deals.

Posted 8/14/2026full-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 technical pre-sales processes and architecting solutions across compute, networking, and storage. Proficient in managing complex B2B sales cycles and developing TCO models while collaborating with cross-functional teams.

Highest-signal resume keywords

Sales Engineering LeadershipTechnical Pre-Sales ProcessesNVIDIA Compute PlatformsB2B Sales Cycle ManagementTCO/ROI Model Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Distributed TrainingProduction Inference ML WorkloadsPyTorchNVIDIA AI EnterpriseTCO ComparisonsGPU OrchestrationKubernetesData PipelinesBenchmarkingCapacity Planning

Soft Skills

CoachingCollaborationInfluencingPlayer-Coach MindsetCommunication

Tools & Technologies

NCCLCUDAInfiniBandSpectrum-X EthernetRDMA/RoCEDGX SystemsHGX SystemsMGX SystemsRun:aiNeMo

Industry Keywords

B2B SalesAI InfrastructureData-Center EconomicsHPCSovereign AI Buyers

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 and build reusable discovery, architecture, TCO, benchmark, POV, demo, and RFP processes
Set and maintain technical quality standards and run enablement
Partner with Account Executives as technical lead on strategic and enterprise opportunities from discovery through technical close
Run MEDDPICC or equivalent qualification and develop technical win plans
Architect solutions across compute, networking, storage, and orchestration
Produce sizing, capacity plans, and TCO comparisons against hyperscalers and self-build alternatives
Design and drive proofs of concept and proofs of value, including benchmarks and success criteria
Feed product and capacity requirements to product, platform, and supply/capacity planning teams
Work with NVIDIA field and partner ecosystems on reference architectures and joint pursuits
Influence product roadmap and packaging based on field experience
Travel meaningfully to customers, data centers, and NVIDIA/partner events

Requirements

What you’ll need

Hands-on experience running or standing up distributed training and/or production inference ML workloads
Practical fluency across data pipelines, distributed training, fine-tuning, and serving
Understanding of interconnect, memory bandwidth, I/O, and scheduling bottlenecks
Proficiency with PyTorch and related tooling, including NCCL, CUDA concepts, containers, and schedulers
Deep knowledge of NVIDIA compute platforms, including Hopper and Blackwell generations and DGX, HGX, and MGX systems
Knowledge of NVLink/NVSwitch, InfiniBand, Spectrum-X Ethernet, RDMA/RoCE, and DPUs
Familiarity with NVIDIA AI Enterprise, NIM, NeMo, Triton/TensorRT-LLM, Base Command, Run:ai, GPU orchestration, and NGC
Understanding of the NVIDIA Cloud Partner motion and co-selling
Track record supporting complex B2B sales cycles of 6–18+ months with large ACV/TCV
Experience with multi-stakeholder enterprise sales involving technical, procurement, finance, security, and executive stakeholders
Ability to build and defend TCO/ROI models and translate benchmarks into commercial value
Experience hiring, developing, and leading a sales engineering or solutions architecture team, or clear readiness to do so
Player-coach mindset and credibility in complex technical deals
Strongly preferred: experience selling GPU cloud, HPC, or specialized infrastructure
Strongly preferred: Kubernetes, GPU operators/device plugins, Slurm, multi-cluster management, and virtualized GPU/KubeVirt patterns
Strongly preferred: storage-for-AI literacy, data-center economics, and exposure to sovereign, regulated, or government AI buyers

Benefits

Comp & perks

Competitive compensation package with a strong benefits plan
Professional development and training
Attend conferences and working groups
Company outings
Happy hours
Hackathons
Tech talks
Remote work option
Equity
Variable compensation tied to team bookings/attainment