Solution Specialist, AI Runtime Services

Overview As a Solution Specialist for AI Runtime Services, you’ll bring CoreWeave’s AI runtime offerings to new enterprise and research customers, shaping adoption and guiding product feedback. You’ll translate needs around model serving, batching, and sandboxed execution into scalable playbooks and roadmap input. The role sits at the intersection of sales, engineering, and solution architecture, enabling production-scale AI deployments. You’ll work to prove value, document performance tradeoffs, and help close strategic opportunities. Compensation / BenefitsMedical, dental, and vision insurance - 100% paidCompany-paid Life InsuranceDiscretionary bonus and equity awards401(k) with employer matchFlexible PTOTuition Reimbursement ResponsibilitiesOwn the commercial and technical strategy for net-new AI runtime customer winsDrive opportunities where latency, throughput, or isolation impede scalingDevelop expertise in AI runtime landscape using Inference and Sandboxes as flagship examplesTranslate customer requirements into actionable product feedback for roadmapCreate deal structures, technical playbooks, and benchmark narratives to accelerate opportunitiesEngage with enterprise and research buyers as the normative voice on performance and costDesign commercial framework for large-scale deployments, including throughput and SLA considerationsCollaborate with product and infrastructure teams to maintain competitive edge across deployments Key requirements10+ years in distributed systems, ML infrastructure, or production AI engineering5+ years in AI runtime systems in customer-facing rolesDeep knowledge of AI workloads runtime, including serving frameworks and batchingExperience with sandboxed/isolation environments and multi-tenant schedulingStrong understanding of GPU memory hierarchies and performance leversFamiliarity with Kubernetes-native runtime orchestrationAbility to translate business strategy into technical realityBridge between sales strategy and ML infrastructure engineeringStrong stakeholder management across multi-persona evaluationsModel serving architecturesExecution schedulingContainerized AI workloads