[Hiring] Senior Solutions Architect/Sales Engineer - AI Infrastructure @Liqid

Role Description

We are hiring a Sr Solutions Architect to lead technical pre-sales engagement for Liqid’s most important customer opportunities. This is a senior individual contributor role for someone who can earn credibility with technical buyers, shape the solution architecture, lead proof-of-concept work, and turn complex infrastructure challenges into clear customer value.

This is a hands-on, customer-facing position. You will partner closely with account executives on discovery, design, PoCs, RFP responses, and executive technical conversations. You will own the technical strategy across your territory from early qualification through technical close, reporting to the SVP of Worldwide Solutions Engineering and working directly with a small, senior SA team. The location is primarily California with west mid-west travel.

The right person can whiteboard a GPU inference cluster with a customer’s principal engineer, run a disciplined PoC against agreed success criteria, and translate the technical outcome into business language that resonates with a CFO, CIO, or program office.

Customers will rely on this role to help them rethink GPU infrastructure as HBM, power, and utilization become primary constraints. The Solutions Architect translates Liqid’s capabilities into practical customer architectures, turns ambiguous requirements into deliverable solutions, and feeds field insight back into the evolution of the product.

Key Responsibilities

• Own the technical relationship for accounts across your territory from first discovery through technical close to deployment.
• Partner with your AE on every meaningful opportunity: technical discovery, solution design, PoC scoping, RFP/RFI response, and executive technical engagement.
• Run technical discovery that gets to the real requirement GPU utilization targets, workload mix, fabric constraints, and the economics behind the decision not just the stated spec.
• Be accountable for technical win rate and PoC conversion in your territory, with clear visibility into where deals are won, lost, or stuck.
• PoC’s will be delivered physically with Liqid hardware at a customer or partner venue. Also via a remote lab environment, hosted by Liqid.
• Full ownership of the PoC process is expected including installation, configuration and testing.
• Scope, run, and win proofs of concept against explicit, customer-agreed success criteria and drive them to a decision.
• Build and deliver compelling demos of Liqid Matrix tailored to the workload in front of you: AI training, inference at scale, HPC, and virtualization / VDI.
• Contribute to and reuse the team’s library of reference architectures, sizing tools, TCO models, and PoC playbooks so the team scales faster than headcount and what worked once is repeatable.
• Maintain credible working knowledge of the demo and lab environment and feedback what the field needs from it.
• Operate as a true partner to sales. Help qualify hard, shape the technical strategy on a deal, and know when to walk away from a poor-fit opportunity.
• Carry a territory-ownership mindset: understand your accounts, your competitive landscape, and your pipeline well enough to be proactive rather than reactive.
• Engage credibly across the customer org from a principal engineer or chief architect to a CIO or CFO buying committee and adjust the message to the audience.
• Be a structured voice of the customer back to Product and Engineering translating field signal into prioritized, evidence-backed input rather than one-off anecdotes.
• Working closely with support, marketing and our OEM partners to educate, optimise and develop capabilities.
• Surface roadmap gaps and integration priorities (e.g. SLURM, Kubernetes, virtualization / VDI, northbound integrations) clearly and early, with the customer context that makes them actionable.
• Help close the loop between field PoCs and engineering on early-access and design-partner work.
• Ensure a clean technical hand-off from pre-sales to deployment, customer success, and support so what the customer was sold matches what gets delivered.
• Stay engaged on your most strategic deployments to protect against technical churn and to surface expansion opportunities.

Qualifications

• 5+ years in technical pre-sales, solutions architecture, or field engineering at a B2B infrastructure, systems, or AI infrastructure company.
• A track record of personally owning the technical work on competitive deals discovery, architecture, PoC, and technical close.
• Experience working alongside or through tier-one OEMs (Dell, HPE, Cisco, Supermicro, Lenovo, or equivalents) is a strong plus.

Requirements

• Solid, current knowledge of AI/ML infrastructure: GPU systems (H100/H200/B100/B200 class and successors), the memory hierarchy, and cluster-scale architecture for training and inference.
• Working command of the modern data center stack: server platforms, fabric and networking (CXL, PCIe Gen5/6, NVLink, InfiniBand, RoCE/Ethernet), and storage.
• Hands-on familiarity with virtualization and VDI (VMware and equivalents) and orchestration / scheduling (Kubernetes, SLURM) and an honest sense of where each is mature and where it isn’t.
• Credible on the economics of infrastructure GPU utilization, cost-per-token, TCO not just the bits and bytes.

Benefits

• Base compensation: $155k-175k depending on experience, plus unlimited commission
• Generous Medical/Dental/Vision/Life/Disability benefits package
• 401K
• Unlimited PTO
• Paid Company Holidays
• Cell phone stipend
• Flexible, casual work environment

Company Description

Liqid is doing for AI infrastructure what VMware did for x86 servers, bringing the agility of the cloud to the on-prem datacenter to dynamically allocate and share GPUs, memory, and other resources. This functionality is critical in running AI and advanced-data workloads in production (a.k.a. Inference).

Liqid Matrix software enables global organizations to accelerate the adoption of AI, HPC and VDI by unlocking the bottleneck of GPU utilization and availability through dynamic pooling and sharing of AI resources for higher performance with less total cost, less power, and less hardware.