AI Compute Sales Lead

About Parallel Works

Parallel Works builds and operates ACTIVATE, a control plane for high performance computing and AI. Our customers run large scientific and AI workloads across their own on-premises clusters, Government and commercial cloud, and commercial GPU providers, and ACTIVATE gives them one way in to all of it. The high security boundary is authorized at Impact Level 5, with FIPS validated cryptography and STIG hardening throughout.

The work reaches most fields that depend on computing at scale: weather and climate forecasting, defense and intelligence programs, aerospace and structural analysis, molecular and materials science, energy, and AI research. A quarter here can include standing up a GPU cluster for one of those communities, federating a laboratory's existing on-premises system with burst capacity it did not have before, and getting a domain code written decades ago to run on current hardware.

Customer success sets our priorities. We are a small engineering company, so engineers here work directly with the people using the systems and carry a problem from the first report through to the fix. This is what we call mission engineering: understanding what a customer is trying to accomplish and why the computing matters to it.

About the role

Parallel Works is hiring an AI Compute Sales Lead to grow the managed GPU business. We run managed GPU clusters where a provider supplies the hardware and ACTIVATE is the control plane and support layer on top. In each case the customer wanted usable GPU capacity without building a cluster operations team of its own.

The market is research institutions, Federal laboratories, AI companies, and commercial research and development groups. Parallel Works is provider agnostic and federates on-premises GPU clusters, NeoCloud capacity, and hyperscaler regions under one control plane, so the product sold is the operating layer and the support behind it. Many of these customers already own GPUs on site and want burst capacity that does not split their user base. There is no established pipeline in this segment yet, so building one is the first task.

What you will do

• Generate pipeline: build the funnel from a standing start through outbound, research community networks, conferences, and partner channels.
• Sell with providers: run co-selling motions with GPU providers and hyperscalers, where the provider brings capacity and we bring the managed platform.
• Technical discovery: qualify a workload well enough to scope it: cluster size, GPU generation, interconnect, storage throughput, framework and job pattern, Slurm or Kubernetes, and what the customer already owns.
• Hybrid deals: combine a customer's existing on-premises GPU cluster with reserved NeoCloud capacity and hyperscaler burst under one control plane. These are the largest engagements in the segment.
• Commercial case: work the GPU economics: owned hardware against rented capacity, reserved against on demand, price per GPU hour, realistic utilization, and chargeback across research groups.
• Run the deal: manage multi-stakeholder cycles across research leadership, central IT, security, procurement, and finance.

Requirements

• 5 or more years selling infrastructure, cloud, GPU capacity, or AI platform software, with quota attainment you can describe.
• GPU capacity economics: how on demand, reserved, and committed capacity differ commercially, and how a customer's own cluster compares to rented capacity once utilization and operations staff are counted.
• Leading a technical discovery conversation about training and inference workloads without an engineer present.
• Partner led or co-selling motions.
• Generating your own pipeline instead of working inbound leads.
• You do not need every item on this list. If you have most of it and work well with other people, apply.

Preferred Qualifications

• NeoCloud or GPU as a service market experience.
• Sales into research institutions, national laboratories, university research computing, or AI labs.
• Familiarity with Slurm and Kubernetes as delivery models.
• Federal or public sector exposure, including how those procurement timelines differ from commercial ones.
• Time at an early stage or small company, where the seller also carries qualification and follow through.