Join a seed-stage AI infrastructure company building large-scale training and inference platforms previously accessible only to hyperscalers. The business began with a single managed GPU cluster that reached capacity almost immediately and has since expanded into a global platform spanning infrastructure, networking, and orchestration.

You lead technical discovery, run best-in-class demos and proofs-of-concept, and partner closely with Sales to drive successful evaluations and long-term customer expansions.

This is a pre-sales-forward role for someone who can translate customer needs into clear technical validation, build trust with both executives and engineers, and help create a repeatable technical sales motion across a full-stack AI platform (hardware + software).

If you are interested in this opportunity, get in touch today!

• Partner with Sales to qualify opportunities, lead technical discovery, and define evaluation plans aligned to customer success criteria.
• Design and deliver compelling end-to-end demos, tailoring messaging for both technical and executive audiences.
• Own the full technical POC lifecycle: scope definition, success metrics, architecture, timelines, stakeholder alignment, and production handoff.
• Build strong relationships with customer engineering leaders, platform teams, and infrastructure/security owners.
• Translate technical requirements into solution designs tied directly to business outcomes (latency, throughput, cost, reliability, security).
• Create high-quality enablement assets (demo environments, evaluation playbooks, reference architectures, FAQs) to scale sales effectiveness.
• Provide structured, high-signal feedback to Product, Engineering, and Research teams based on customer objections, feature gaps, and competitive insights.

• 5+ years in a customer-facing technical role, including 2+ years in pre-sales (Sales Engineer, Solutions Architect, or similar).
• Proven experience running complex technical evaluations end-to-end and influencing outcomes in enterprise sales cycles.
• Excellent communication skills - able to explain infrastructure and AI concepts to both technical and non-technical stakeholders.
• Strong knowledge of AI/ML infrastructure and GPU-based systems, including integration with HPC environments.
• Solid understanding of training, fine-tuning, and inference workflows for open-source LLMs.
• Proficiency in Python and JavaScript, with experience building prototypes against APIs.
• Familiarity with Kubernetes, SLURM, Docker/containers, and infrastructure automation or IaC tools (eg, Ansible).

• IPO Equity

• $300,000 per year (Higher depending on experience)