Principal Solutions Specialist, Inference
CoreWeave
- Location
- Remote: Dallas, Texas, United States | Manhattan, New York, United States | Bellevue, Washington, United States | Livingston, New Jersey, United States | Sunnyvale, California, United States | San Francisco, California, United States
- Level
- Staff
- Salary
- $177K–$237K / yr
- Posted
- November 10, 2026
- Source
- Built In
Job description
CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. Learn more at www.coreweave.com.
What You'll Do
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. Runtime services compose the platform our customers use to apply AI to new solutions, and integrate AI models into production applications.
The AI infrastructure market is moving faster than any playbook can keep up with, and the companies building at its frontier, from early-stage North American startups to the world's largest enterprises with operations across AMER, need a commercial partner who understands their infrastructure requirements as instinctively as they do. As a Principal Solution Specialist for Inference in AMER, you open new market opportunities across the execution layer and drive the initial adoption of these offerings with the earliest customers and industries to need them. You channel what you learn into the product roadmap, and scale your insights to make the broader sales and solution architecture organization fluent in the value runtime services create for new customers.
About the role
As a Principal Solution Specialist for Inference in AMER, you work at the leading edge of how CoreWeave brings new inference offerings to market. Rather than running an existing motion, you help create one: you take newly launched services like CoreWeave Serverless and Dedicated Inference into new accounts and new industries in AMER, 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 in the region. You are the field's authoritative voice back to engineering and help shape how CoreWeave builds and goes to market across AMER.
In this role, you will
- Serve as the AMER field's primary intelligence source on how AI companies across North American industries and company stages think about and consume inference, translating regional customer conversations into direct input for CoreWeave's global product, engineering, capacity planning, finance, and marketing teams.
- Drive the commercial and technical strategy in AMER for net new customer wins in inference, where execution performance, deployment flexibility, and operational reliability are the primary buying triggers.
- Identify new business opportunities in AMER where inference performance, throughput optimizations, ephemeral compute, model choice, or operational complexity are barriers to scaling AI on CoreWeave.
- Develop deep expertise across the AI runtime landscape (AI applications, model selection, model serving architectures, containerized AI workloads), using CoreWeave's Inference as a flagship example of what best-in-class runtime looks like.
- Translate customer requirements around serving frameworks (e.g., vLLM, SGLang, TensorRT-LLM), serverless endpoints, agentic requirements, and model customization into specific product feedback that shapes the inference roadmap.
- Develop deal structures, technical playbooks, and benchmark narratives that help AMER sales and SA teams accelerate runtime-sensitive opportunities across the full spectrum of AI deployment patterns.
- Engage directly with AMER customers 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.
- Partner with global product specialists to build integrated deal proposals that reflect the full value of CoreWeave's infrastructure platform for AMER customers.
Who You Are
- 10+ years of experience in distributed systems, ML infrastructure, or production AI applications, with a track record of applying that expertise to drive customer outcomes and revenue in AMER markets.
- 5+ years working with AI runtime systems (model serving, inference optimization, containerized workload execution, model routing, agentic workloads) in customer-facing or deal-shaping capacity.
- Working knowledge of what AI applications need in production, such as frameworks, batching strategies, memory management, and the performance levers that determine throughput and latency at scale.
- Deep understanding of how AI companies across AMER, from startups building their first production systems to enterprises scaling global AI deployments, evaluate, procure, and operationalize AI models through inference platforms, including the technical, financial, regulatory, and organizational dynamics that drive those decisions across key AMER markets.
- 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 for the AMER market with AI technologies, such as generative AI applications, autonomous systems, agents, 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.
- Strong understanding of how enterprises evaluate inference platforms based on factors like cost, latency, and scalability
- Familiarity with Kubernetes-native runtime orchestration (autoscaling, schedu