Account Executive, AI Natives

GMI Cloud is a GPU cloud built for AI teams that need serious compute, fast. We are an NVIDIA Reference Architecture NeoCloud provider headquartered in Mountain View, CA, and rated

Silver in SemiAnalysis ClusterMAX 3.0 (September 2026) , alongside Azure and Lambda.

We run dedicated GPU clusters (H100, H200, B200, B300, GB300, with Vera Rubin coming) across data centers in the US and Asia, and we serve production LLM inference through our own platform and on OpenRouter. Our customers are AI-native startups and research labs building foundation models, video and speech generation, robotics, and inference products.

What you'll do

Build your own pipeline.

Map the Bay Area's AI-native ecosystem (neolabs, robotics, video and voice generation, AI-for-science, inference platforms) and open doors through outbound, events, investors, and research communities in English and Chinese.

Sell to the people who run the GPUs.

Work directly with founders, CTOs, Heads of Infrastructure, and ML researchers. Understand what they are training or serving, and why their current provider is not enough.

Run fast, technical evaluations.

Partner with our Solutions Architects and SREs on benchmarks, test clusters, and proofs of concept, and turn them into committed contracts.

Structure and close multi-year capacity deals.

Scope cluster size, GPU generation, networking and storage, delivery dates, term, prepayment, and SLAs. Negotiate against aggressive market pricing while protecting margin.

Line up supply before you sell it.

Work with our infrastructure and data-center teams to confirm capacity and delivery timelines, so every commitment you make is one we can keep.

Land and expand.

Grow accounts from a first cluster into repeat orders and new regions, together with our account management and support teams.

Be our ears in the market.

Bring back competitor pricing, customer needs, and GPU supply signals to shape our pricing, capacity planning, and product roadmap.

Keep a clean pipeline.

Forecast accurately and keep the CRM up to date.

What we're looking for

We welcome two kinds of candidates. You do not need a sales background if you bring deep technical experience and want to move into a commercial role.

Path A: you have sold infrastructure

4+ years of quota-carrying B2B sales in GPU cloud, cloud infrastructure, HPC, or AI/ML platforms, with a record of beating your number.

You have closed complex, multi-stakeholder deals with technical buyers. Multi-year or seven-figure contracts are a strong plus.

You have sold to startups or research teams, not only to enterprise procurement.

Path B: you have built or supported GPU infrastructure, and want to sell it

5+ years in a customer-facing technical role, such as Solutions Architect, Sales Engineer, Technical Account Manager, or Customer/Field Engineer, at a GPU cloud, hyperscaler, NVIDIA partner, or AI infrastructure company.

Hands-on experience with GPU clusters, Kubernetes, and infrastructure: sizing clusters, running benchmarks or POCs, or supporting customers' training and inference workloads.

A clear, genuine drive to own revenue and a customer relationship end to end. We will pair you with our sales leadership and invest in your growth.

Everyone

Able to work on-site in Mountain View, CA, four days a week , and travel to customer sites around the Bay Area and occasionally beyond.

Technical fluency in AI compute: you can explain the difference between a training cluster and an inference deployment, and why GPU generation, interconnect (InfiniBand or RoCE), and storage matter to a customer.

Comfort with ambiguity. You can win without a finished playbook, create your own materials, and get things unblocked inside a fast-moving startup.

Clear, direct communication with founders and researchers, and strong follow-through.

Nice to have

An existing network among AI-native founders, infrastructure leads, ML researchers, or AI-focused investors in the Bay Area, and in the Chinese-speaking AI research community.

Experience at a neocloud or GPU-first company (for example, CoreWeave, Nebius, Lambda, Crusoe, Together AI) or on a hyperscaler or NVIDIA team focused on AI startups.

Hands-on familiarity with NVIDIA Hopper and Blackwell systems (H200, B200, B300, GB200/GB300 NVL72), Slurm or Kubernetes for AI workloads, and LLM training, fine-tuning, or inference.

Experience structuring capacity commitments: reserved versus on-demand, prepayment, term discounts, delivery milestones.

Early-stage startup experience, including building a go-to-market motion from zero.

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