Solution Specialist, AI Runtime Services

CoreWeave · Livingston, NJ · 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 motion for newly launched services like Inference and Sandboxes into new accounts and new industries.
Prove the value with the first wave of customers operationalizing AI at scale.
Establish the playbooks sales and solution architects use to repeat those wins.
Own the commercial and technical strategy for net new customer wins in AI runtime infrastructure.
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).
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.

Ability to benchmark, explain, and commercially position runtime performance differences across deployment patterns, instance types, and serving configurations.

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.

Qualifications

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.

Familiarity with cost-per-token economics, inference fleet optimization, and the commercial tradeoffs between on-demand, reserved, and spot GPU capacity for runtime workloads.

Experience with Kubernetes-native runtime orchestration (autoscaling, scheduling policies, GPU operators) and how it impacts workload portability, operational complexity, and platform stickiness.

Skills

Ability to translate boardroom goals into technical reality: you can explain high-level business strategy and low-level runtime performance tradeoffs in the same meeting.

Expertise in acting as the bridge: between high-level sales strategy and deep ML infrastructure engineering.

Able to navigate "The Room": you have a proven ability to manage complex, multi-stakeholder technical evaluations without losing momentum.

Benefits

Medical, dental, and vision insurance - 100% paid for by CoreWeave
Company-paid Life Insurance
Voluntary supplemental life insurance
Short and long-term disability insurance
Flexible Spending Account (FSA)
Health Savings Account (HSA)
Tuition Reimbursement
Ability to participate in Employee Stock Purchase Program (ESPP)
Mental Wellness Benefits through Spring Health
Family-Forming support provided by Carrot
Paid Parental Leave
Flexible, full-service childcare support with Kinside
401(k) with a generous employer match
Flexible PTO
Catered lunch each day in our office and data center locations
A casual work environment
A work culture focused on innovative disruption

Pay

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.

Schedule

We are looking for someone who can work remotely and travel up to 20%.