Solution Specialist (AI Runtime Services)

Join CoreWeave as a Solution Specialist for AI Runtime Services. In this role, you will be at the forefront of bringing new runtime offerings to market, establishing playbooks for sales and solution architects, and translating customer requirements into product feedback. You will drive new business opportunities, develop deep expertise in the AI runtime landscape, and engage directly with enterprise and research buyers. This position requires a strong background in AI runtime systems, distributed systems, and technical sales.

• Prove the value of newly launched services like Inference and Sandboxes to new accounts and industries.
• Drive new business opportunities where inference latency, throughput bottlenecks, workload isolation requirements, or operational complexity are barriers to scaling AI.
• Develop deep expertise across the AI runtime landscape and translate customer requirements into specific product feedback that shapes the AI Runtime Services roadmap.
• 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
• You love acting as the bridge: between high-level sales strategy and deep ML infrastructure engineering
• Wondering if you're a good fit? We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams, even if you aren't a 100% skill or experience match. Here are a few qualities we've found compatible with our team. If some of this describes you, we'd love to talk
• 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 translating boardroom goals into technical reality: you can explain high-level business strategy and low-level runtime performance tradeoffs in the same meeting