Solution Specialist (AI Runtime Services)

Responsibilities As a Solution Specialist for AI Runtime Services, you work at the leading edge of how CoreWeave brings new runtime offerings to market Rather than running an existing motion, you create one: you take newly launched services like Inference and Sandboxes into new accounts and new industries, prove their value with the first wave of customers operationalizing AI at scale, and establish the playbooks sales and solution architects use to repeat those wins You are the field’s authoritative voice back to engineering, translating what early adopters need around serving frameworks, batching, and execution isolation into the priorities that shape the AI Runtime Services roadmap Own the commercial and technical strategy for net new customer wins in AI runtime infrastructure, where execution performance, deployment flexibility, and operational reliability are the primary buying triggers Drive new business opportunities where inference latency, throughput bottlenecks, workload isolation requirements, or operational complexity are barriers to scaling AI on CoreWeave Develop deep expertise across the AI runtime landscape (model serving architectures, execution scheduling, containerized AI workloads, and secure multi-tenant compute), using CoreWeave’s Inference and Sandboxes products as flagship examples of what best-in-class runtime looks like Translate customer requirements around serving frameworks (e.g., vLLM, TensorRT-LLM, TGI), batching strategies, and execution isolation into specific product feedback that shapes the AI Runtime Services roadmap Develop deal structures, technical playbooks, and benchmark narratives that help sales and SA teams accelerate runtime-sensitive opportunities across the full spectrum of AI deployment patterns Engage directly with enterprise and research buyers as the authoritative voice on runtime performance tradeoffs, cost-per-token economics, and the architectural decisions that separate prototype deployments from production-scale AI systems Design the commercial framework for large-scale runtime deployment deals, including throughput modeling, GPU utilization commitments, and SLA structures that support enterprise closings Partner with product and infrastructure teams to maintain a competitive edge on serving efficiency, execution isolation, and operational reliability across active and prospective customer deployments Qualifications Familiarity with Kubernetes-native runtime orchestration (autoscaling, scheduling policies, GPU operators) and how it impacts workload portability, operational complexity, and platform stickiness 5+ years working with AI runtime systems (model serving, inference optimization, containerized workload execution, or real-time ML pipelines) in a customer-facing or deal-shaping capacity Deep working knowledge of how AI workloads execute at runtime: serving frameworks, batching strategies, GPU memory management, and the performance levers that determine throughput and latency at scale (with specific familiarity with products like vLLM, TensorRT-LLM, or Triton) Strong understanding of GPU memory hierarchies, model parallelism strategies, and how runtime architecture decisions translate into cost, latency, and scalability outcomes for enterprise customers Experience with sandboxed and isolated execution environments (microVM architectures, container runtimes, secure multi-tenant scheduling) and how execution isolation requirements shape platform selection decisions 10+ years of experience in distributed systems, ML infrastructure, or production AI engineering, with a track record of applying that expertise to drive customer outcomes and revenue Ability to benchmark, explain, and commercially position runtime performance differences across deployment patterns, instance types, and serving configurations Experience driving new business or shaping product strategy in industries with high-throughput AI runtime demands, such as generative AI applications, autonomous systems, financial modeling, or developer platforms Prior background in technical sales, solution consulting, or product management supporting large-scale inference infrastructure or AI platform decisions Advanced degree in Computer Science, Machine Learning, or Engineering, or equivalent experience with a demonstrated ability to operate at the intersection of technical architecture and commercial strategy Deep understanding of cost-per-token economics, inference fleet optimization, and the commercial tradeoffs between on-demand, reserved, and spot GPU capacity for runtime workloads Desired Qualities You are an expert at navigating "The Room": you have a proven ability to manage complex, multi-stakeholder technical evaluations without losing momentum You love acting as the bridge: between high-level sales strategy and deep ML infrastructure engineering You love translating boardroom goals into technical reality: you can explain high-level business strategy and low-level runtime performance tradeoffs in the same meeting #J-18808-Ljbffr CoreWeave