3 months ago
Base Salary
$207k - $275k/yr
Responsibilities
- Own the commercial and technical strategy for net-new customer wins involving AI runtime infrastructure.
- Drive opportunities where inference latency, throughput bottlenecks, workload isolation, or operational complexity limit AI scaling.
- Develop expertise in model serving architectures, execution scheduling, containerized AI workloads, and secure multi-tenant compute.
- Translate customer requirements involving serving frameworks, batching, and execution isolation into product roadmap feedback.
- Create deal structures, technical playbooks, and benchmark narratives for sales and solution architecture teams.
- Advise enterprise and research buyers on runtime performance, cost-per-token economics, and production-scale AI architecture.
- Design commercial frameworks for large-scale runtime deployments, including throughput models, GPU utilization commitments, and SLA structures.
- Partner with product and infrastructure teams to improve serving efficiency, execution isolation, and operational reliability.
Requirements
- 10+ years of experience in distributed systems, ML infrastructure, or production AI engineering with a record of driving customer outcomes and revenue.
- 5+ years of experience with AI runtime systems in a customer-facing or deal-shaping capacity.
- Deep knowledge of model serving, batching, GPU memory management, throughput, latency, and runtime performance, including familiarity with vLLM, TensorRT-LLM, or Triton.
- Experience with sandboxed and isolated execution environments, microVM architectures, container runtimes, and secure multi-tenant scheduling.
- Understanding of GPU memory hierarchies, model parallelism, and the cost, latency, and scalability effects of runtime architecture decisions.
- Familiarity with Kubernetes-native runtime orchestration, autoscaling, scheduling policies, and GPU operators.
- Ability to benchmark and commercially position runtime performance across deployment patterns, instance types, and serving configurations.
- Preferred experience driving new business or product strategy for high-throughput AI runtime use cases.
- Preferred background in technical sales, solution consulting, or product management for large-scale inference infrastructure or AI platforms.
- Preferred understanding of cost-per-token economics, inference fleet optimization, and on-demand, reserved, and spot GPU capacity tradeoffs.
- Advanced degree in Computer Science, Machine Learning, or Engineering, or equivalent experience, is preferred.
Benefits
- Medical, dental, and vision insurance fully paid by CoreWeave for US-based employees.
- Company-paid life insurance, voluntary supplemental life insurance, and short- and long-term disability insurance.
- Flexible Spending Account and Health Savings Account.
- Tuition reimbursement and eligibility to participate in the Employee Stock Purchase Program.
- Mental wellness benefits through Spring Health and family-forming support through Carrot.
- Paid parental leave and flexible, full-service childcare support through Kinside.
- 401(k) with an employer match and flexible paid time off.
- Catered lunch at office and data center locations.
- Casual work environment focused on innovative disruption.
Tech Stack
Categories
Solutions Engineering
About CoreWeave
CoreWeave provides a GPU-accelerated cloud for AI training and inference, VFX, and rendering, with bare-metal instances, Kubernetes orchestration, and managed services to scale workloads. It sells on-demand and reserved capacity to AI labs, startups, and enterprises, and offers SaaS tools and hands-on support for deployment. Founded in 2017 and headquartered in New York, it is publicly traded on Nasdaq under the ticker CRWV.