Solution Specialist, AI Runtime Services

CoreWeave · New York, NY · 2 wk ago

Business Development$207k–$275k/yrFull-time

About the role

As CoreWeave turns raw GPU capacity into production-grade AI services, it is launching new ways for customers to run, scale, and serve AI workloads, and those new services need a market-maker. As a Solution Specialist for AI Runtime Services, you open new market opportunities across the execution layer (high-throughput, low-latency model serving through our Inference platform, and secure, isolated execution through Sandboxes) and drive the initial adoption of these offerings with the earliest customers and industries to need them.

Responsibilities

Work at the leading edge of how CoreWeave brings new runtime offerings to market.
Create new market 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).
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.

Requirements

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. 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). Experience with sandboxed and isolated execution environments (microVM architectures, container runtimes, secure multi-tenant scheduling) and how execution isolation requirements shape platform selection decisions. Strong understanding of GPU memory hierarchies, model parallelism strategies, and how runtime architecture decisions translate into cost, latency, and scalability outcomes for enterprise customers. Familiarity with Kubernetes-native runtime orchestration (autoscaling, scheduling policies, GPU operators) and how it impacts workload portability, operational complexity, and platform stickiness. Ability to benchmark, explain, and commercially position runtime performance differences across deployment patterns, instance types, and serving configurations. Preferred 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. - 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. - 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.

Qualifications

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. 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. 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). Experience with sandboxed and isolated execution environments (microVM architectures, container runtimes, secure multi-tenant scheduling) and how execution isolation requirements shape platform selection decisions. Strong understanding of GPU memory hierarchies, model parallelism strategies, and how runtime architecture decisions translate into cost, latency, and scalability outcomes for enterprise customers. Familiarity with Kubernetes-native runtime orchestration (autoscaling, scheduling policies, GPU operators) and how it impacts workload portability, operational complexity, and platform stickiness. Ability to benchmark, explain, and commercially position runtime performance differences across deployment patterns, instance types, and serving configurations. Preferred 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. - 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.

Skills

Strong communication and presentation skills. Ability to navigate complex technical evaluations. Experience with AI runtime systems (model serving, inference optimization, containerized workload execution, or real-time ML pipelines). Familiarity with Kubernetes-native runtime orchestration (autoscaling, scheduling policies, GPU operators). Experience with sandboxed and isolated execution environments (microVM architectures, container runtimes, secure multi-tenant scheduling). Understanding of GPU memory hierarchies, model parallelism strategies, and how runtime architecture decisions translate into cost, latency, and scalability outcomes for enterprise customers. Ability to benchmark, explain, and commercially position runtime performance differences across deployment patterns, instance types, and serving configurations. Preferred Skills: - 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. - 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.

Benefits

The base salary range for this role is $207,000 to $275,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).

Pay

Schedule

Full-time.