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VClusterLabs

Sr. Partner Solutions Engineer

United StatesRemote · United States
Senior · FullTime · Customer Engineering
POSTED 22 JULVERIFIED 22H AGO
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Role
109 days opentrue age
Posted 22 Jul
Payats field
$140k–180kposted
▼ −13% vs market median $183k
$140k$210k
Market p25–p75
$160k–$210k
United States · n=3913 · 365d to 6 Nov
CompanyVClusterLabs →
38open roles
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17,056open · Senior Software engineering
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74,990open
All tracked boards · live 8 Nov · n=17,056
Kubernetes

As a Senior Partner Solutions Engineer at vCluster Labs, you aren’t just supporting partner deals; you are building the technical foundation of our partner ecosystem from the ground up. You will be our first dedicated partner-facing pre-sales engineer, turning relationships with NVIDIA, WWT, AMD, and AI Cloud providers like Nebius into validated reference architectures, enabled partner architects, and partner-sourced pipeline. Where there’s a GPU, vCluster needs to be there, and you are the person who makes that real: someone who can whiteboard a GPU cloud architecture with an NVIDIA solution architect in the morning and train a room of partner sellers in the afternoon.

As a Senior Partner Solutions Engineer, your role will include

Joint Reference Architectures: Design and validate reference architectures with strategic partners, from NVIDIA blueprints and AMD foundations up through the platform layer: vCluster, vNode, vMetal, and vCluster Platform. Turn one successful deployment into a repeatable, sellable solution with bills of material, deployment guides, demo environments, and benchmarks.

Partner Enablement: Build and deliver technical enablement for partner architects and sellers, including training dozens of architects at partner Centers of Excellence on the value proposition, cost, and performance story of the vCluster product family, so partners position us to win when we are not in the room.

Field Enablement: Enable vCluster’s internal field SEs to engage partners effectively, so the partner motion scales beyond one person and the field is never pulled off customers to cover channel work.

Partner-Led Opportunities: Own technical discovery, demos, and Proofs of Concept for partner-sourced opportunities, always in support of direct field ownership. Run a disciplined POC model: validated business problem, named sponsor, clear success criteria, and a defined production milestone.

Centers of Excellence: Stand up vCluster presence inside partner Centers of Excellence, such as WWT’s in St. Louis, tied to named customers and qualified pipeline rather than marketing labs.

Ecosystem Navigation: Build technical credibility across a focused set of 6–8 priority partners, cultivating executive sponsors and technical champions without becoming dependent on a single contact or business unit.

This role could be a fit for you if you bring

Deep Kubernetes expertise: You go deep on cluster architecture, Tenant Isolation, and the cloud-native ecosystem, and you can master vCluster, vNode, and vMetal inside and out. This one is required, not preferred.

Partner pre-sales experience: You have worked with partners (OEMs, GSIs, resellers, or cloud providers) in a technical pre-sales capacity and you understand the partner dynamic: co-selling, joint validation, and winning through someone else’s sellers.

Solutions engineering craft: Years of discovery, whiteboarding, demos, and POCs that actually convert to production, with the qualification discipline to say no to science projects.

Enablement building: You have created enablement content, workshops, hands-on labs, or certification paths from scratch, and the partners you enabled kept winning after you left the room.

Executive presence: You can hold the room with a Kubernetes engineer and a CIO in the same meeting, and you move people you don’t manage.

Bonus points for

NVIDIA ecosystem experience: DGX, Dynamo, GPU Operator, AI Enterprise, or NVIDIA certifications, plus an understanding of how NVIDIA blueprints and partner validation actually work.

AI infrastructure depth: GPU infrastructure, storage, and networking for AI factories and GPU clouds (Weka- and VAST-class storage literacy is a plus).

AI Cloud or GPU-as-a-Service experience: Time building or supporting GPU-as-a-Service or Kubernetes-as-a-Service platforms at AI Cloud providers or service providers.

About vCluster Labs

We're the #1 platform for AI infrastructure, trusted by the world's fastest-growing AI cloud builders. We're a venture-backed startup that's raised over $28M from top-tier investors including Khosla Ventures (first investor in OpenAI, GitLab, Stripe, and DoorDash), and we're in a hyper-growth phase looking for motivated people to join our team. Our headquarters are in San Francisco (Salesforce Tower), but our team is distributed around the globe with a remote-first culture.

Source · VClusterLabsFirst seen 7 NovOriginal ↗