Principal Solution Specialist, Core Services

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: CoreWeave's core infrastructure (bare-metal GPU compute, high-performance networking, and the Kubernetes platform that ties them together) is constantly evolving, and bringing each new capability to market is where this role lives. As a Principal Solution Specialist for Core Services, you identify the new market opportunities where compute performance, network fabric architecture, and platform reliability unlock adoption, and you drive the first wins for CoreWeave's newest core capabilities with the customers and industries that demand them earliest. You turn field insight into direct input on the compute and networking roadmap, and you make the broader sales and solution architecture teams fluent in the value these services create for new customers. About the role: As a Principal Solution Specialist for Core Services, you operate at the front edge of CoreWeave's go-to-market motion for its foundational infrastructure. Your job is to create the motion, not just run it: you take new and evolving core offerings spanning our Compute (bare-metal and virtualized GPU instances) and Networking (high-speed fabrics and interconnects for tight GPU-to-GPU communication) into new accounts and new industries, prove their value with the most demanding early customers, and build the repeatable playbooks sales and solution architects use to scale. You are the field's voice into engineering, translating what early adopters need around cluster topology, RDMA networking, and scheduling into the priorities that shape CoreWeave's compute and networking roadmap. In this role, you will: • Own the commercial and technical strategy for net new customer wins in core AI infrastructure, where GPU compute performance, cluster networking, and platform reliability are the primary buying triggers. • Drive new business opportunities where compute bottlenecks, network bandwidth limitations, or bare-metal performance requirements are the barrier to scaling AI training and inference workloads on CoreWeave. • Translate customer requirements around GPU cluster topology, RDMA networking (InfiniBand, RoCE), and Kubernetes scheduling into specific product feedback that shapes CoreWeave's compute and networking roadmap. • Develop deal structures, technical playbooks, and benchmark narratives that help sales and SA teams accelerate compute-heavy and networking-sensitive opportunities. • Engage directly with enterprise and research buyers as the authoritative voice on GPU cluster architecture, network fabric design, and the performance tradeoffs between instance types and topologies. • Design the commercial framework for large-scale GPU reservation deals, including MFU modeling, cluster sizing, and network bandwidth commitments that support large enterprise closings. • Partner with capacity, infrastructure, and networking teams to maintain a competitive edge on compute density, interconnect performance, and platform reliability across active and prospective customer deployments. Who You Are: • 10+ years of experience in HPC, data center infrastructure, or GPU cluster engineering, with a track record of applying that expertise to drive customer outcomes and revenue. • 5+ years working with large-scale GPU clusters (NVIDIA A100/H100/H200 or AMD MI300X), RDMA networking, and distributed training frameworks in a customer-facing or deal-shaping capacity. • Deep working knowledge of high-speed network fabrics (InfiniBand, RoCE, and NVLink) and how interconnect topology, bandwidth, and latency impact distributed training and inference at scale. • Experience deploying and tuning large-scale Kubernetes clusters for GPU workloads (NVIDIA GPU Operator, MIG, time-slicing) and positioning platform capabilities against customer performance and scalability requirements. • Strong understanding of compute instance types, CPU-GPU memory bandwidth, NVMe storage tiers, and power and cooling considerations as they relate to workload efficiency and enterprise deal structure. • Familiarity with distributed training frameworks (PyTorch FSDP, Megatron-LM, DeepSpeed) and how cluster architecture decisions impact training throughput, fault tolerance, and overall MFU. • Understanding of BGP, peering, and data center interconnect architectures as they relate to multi-region GPU deployments, data gravity, and enterprise network integration. Preferred: • Experience driving new business or shaping product strategy in industries with extreme compute demands, such as large-scale AI model training, genomics, financial risk modeling, or government and defense HPC. • Prior background in technical sales, solution consulting, or product management supporting large-scale GPU cluster procurement or data center infrastructure decisions. • Deep understanding of GPU cluster procurement economics, reserved vs. on-demand capacity tradeoffs, and the commercial dynamics of large-scale compute commitments. • Advanced degree in Computer Science, Electrical Engineering, or a related field, or equivalent experience with a demonstrated ability to operate at the intersection of technical architecture and commercial strategy. Wondering if you're a good fit? 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.