Senior Manager, Sales Engineering — AI / GPU Cloud (NeoCloud)

Description

The company sells accelerated compute to Neoclouds, AI-native startups, enterprise AI teams, research labs, and sovereign or regulated buyers. Its orchestration layer turns disaggregated GPU infrastructure into a multi-tenant, production-ready AI cloud that does not tie customers to one hyperscaler or hardware vendor. Offerings include GPU clusters, bare metal, and managed AI infrastructure.

Deals are technical, high value, and long cycle. They are won on credibility: designing the right cluster, modelling real TCO, proving performance, and reducing the customer's risk of moving onto the platform.

The role owns the technical win. You will build and lead the sales engineering function that turns interested prospects into signed, multi-year committed-capacity contracts, and set the pre-sales standard as headcount and deal volume grow.

The work calls for someone who has actually run training and inference workloads, debated interconnect topology with a customer's ML infrastructure lead, and closed large deals that took quarters rather than weeks. It is not a demo-only role.

The company is part of IREN and is a Kubernetes-native AI infrastructure company. The company is the company name in the posting, and it is not to be named here.

Responsibilities

• Hire, coach, and retain sales engineers and solutions architects, and define the pre-sales operating model as the organisation grows.
• Build reusable assets: discovery frameworks, reference architectures, TCO and benchmark models, POV playbooks, demo and benchmark environments, and RFP response libraries.
• Set and maintain the technical quality bar for the team, and run enablement so every SE can discuss GPU architecture, networking, and orchestration with confidence.
• Act as the technical lead alongside Account Executives on strategic and enterprise deals, from discovery through technical close.
• Qualify using a formal method such as MEDDPICC or an equivalent. Identify the economic buyer, decision criteria, and technical champion, then build the win plan around them.
• Design solutions across compute, networking, storage, and orchestration. Produce sizing, capacity plans, and TCO comparisons against hyperscalers and self-build options.
• Design and run POCs and POVs. Agree success criteria at the start, run the benchmarks, and turn the results into commercial progress.
• Pass structured product and capacity requirements to product, platform, and supply or capacity planning teams.
• Work with the NVIDIA field team and partner ecosystem, including the Cloud Partner programme, reference architectures, and joint pursuits, to strengthen deals.
• Shape the roadmap and packaging using what you learn in the field.

Requirements

• Hands-on experience running AI and ML infrastructure, including distributed training, production inference, or both.
• Working knowledge of the full training and inference lifecycle: data pipelines, multi-node and multi-GPU training, fine-tuning, and serving. You know where bottlenecks sit, such as interconnect, memory bandwidth, I/O, and scheduling.
• Comfortable with PyTorch and the tools around it that customers use, such as NCCL, CUDA concepts, containers, and schedulers.
• Current on NVIDIA's compute stack across the Hopper and Blackwell generations, including H100, H200, GB200 NVL72, B200-class systems, and Grace-Hopper superchips, and the DGX, HGX, and MGX reference systems. Aware of what comes next.
• Fluent in networking: NVLink and NVSwitch domains, InfiniBand (Quantum) versus Spectrum-X Ethernet, RDMA and RoCE, and DPUs. Understands why fabric choice can make or break a large training cluster.
• Familiar with the NVIDIA software layer, including AI Enterprise, NIM, NeMo, Triton and TensorRT-LLM, Base Command, Run:ai and GPU orchestration, and NGC.
• Understands the NVIDIA Cloud Partner model and how to co-sell with NVIDIA.
• Has supported complex B2B deals running 6 to 18+ months with large ACV and TCV, ideally including multi-year committed or reserved capacity.
• Works well with many stakeholders: ML and infrastructure leads, platform engineers, procurement, finance, security, and executive sponsors.
• Can build and defend a TCO or ROI model against hyperscaler and on-prem alternatives, and turn benchmark results into commercial value.
• Has hired, developed, and led a sales engineering or solutions architecture team, or can clearly show readiness to do so, including building process and enablement from a light or greenfield start.
• Acts as a player-coach: credible on the hardest deals while developing others to do the same.
• Strongly preferred: experience selling GPU cloud, HPC, or specialised infrastructure, ideally at a NeoCloud or GPU-cloud provider, a hyperscaler AI organisation, or an accelerated-hardware vendor.
• Strongly preferred: hands-on with cloud-native and cluster orchestration for AI, including Kubernetes with GPU operators and device plugins, Slurm, and multi-cluster management. Familiarity with virtualised GPU or KubeVirt-style patterns is a plus.
• Strongly preferred: storage-for-AI knowledge, including high-throughput parallel and object storage and its role in training pipelines.
• Strongly preferred: understanding of data centre economics and constraints, including power, cooling, rack density, and how capacity availability shapes deals.
• Strongly preferred: exposure to sovereign, regulated, or government AI buyers.

Conditions

Equal opportunity applies; the company welcomes applicants of all backgrounds.

Benefits

• Work at an established Silicon Valley leader in cloud infrastructure, alongside highly capable colleagues who help Fortune 500 and Global 2000 customers adopt next-generation cloud technology.
• Contribute to open-source innovation.
• Join a young, fast-moving company that values openness, collaboration, calculated risk-taking, and continuous growth.
• Access to professional development and training.
• Attend conferences and working groups.
• Team events including outings, happy hours, hackathons, and tech talks.
• Competitive compensation and a strong benefits plan.
• The company is ranked second for Container Management in G2, behind AWS.