cover_letter / art_cKRggYdyEAA

Dear CoreWeave Data Services Hiring Team, CoreWeave is building the essential cloud layer that frontier AI labs and enterprises depend on — not just for compute, but increasingly for the data infrastructure that makes GPU workloads actually run. That intersection of high-performance infrastructure and AI-native data services is where I have spent the last several years, and it is precisely why this Staff PM role caught my attention. At Intuit, scaling the ICE platform to 675M+ engagements and 50K TPS taught me what it means to own infrastructure that other teams treat as critical path — and I want to bring that same orientation to CoreWeave's data services portfolio. ## Technical and Platform Foundation My infrastructure PM background is grounded in the kind of throughput and latency problems CoreWeave's customers face daily. At Intuit, I led the rSocket migration that scaled ICE from 6K to 50K transactions per second while supporting approximately 1.5M concurrent connections at sub-25ms TP99 — a project that required close collaboration with platform architects on service design, SLA definition, and operational readiness across QuickBooks, TurboTax, Mint, Mailchimp, and Credit Karma. I also delivered the ICE Self-Service platform — DevPortal, GitOps configuration, and ICE Playground — reducing developer onboarding from two to three weeks down to minutes in pre-production and under 24 hours for production, while mitigating over $1M in projected opex growth. These are the kinds of platform primitives your JD describes: API-first, developer-adopted, and built to scale. On the data side specifically, I have hands-on experience with PostgreSQL metadata services and streaming architectures from my time at Splunk, where I owned the Search Service (Go microservices), Search Catalog (PostgreSQL metadata service), and Splunk Processing Language. I led a query performance optimization initiative that achieved up to 10x improvements in Splunk Cloud Services search for a beta enterprise customer. At Kaiser Permanente, I led the enterprise rollout of Splunk Logging-as-a-Service handling 1.7 TB of daily log volume across 200+ internal customers, and built caching infrastructure using Redis and XC10 to address scalability, fault tolerance, and data redundancy at scale. Beyond platform PM work, I have built data-intensive AI systems firsthand. My aeval evaluation platform runs a FastAPI orchestrator, TimescaleDB for time-series metric storage, and a Redis job queue — with statistical rigor baked in (bootstrap confidence intervals, Welch's t-test, Cohen's d effect size) and CI/CD integration for automated safety gates. My RL post-training workbench benchmarks GRPO, DPO, PPO, and nine other algorithms across TRL, VeRL, OpenRLHF, and NeMo RL frameworks, with GPU Docker passthrough and live SSE metric streaming — meaning I understand CoreWeave's customer use cases not just as a PM, but as someone who has run training jobs on GPU clusters and cared deeply about throughput, memory, and convergence metrics. ## Why This Role The Data Services team is solving the problem that sits one layer below the GPU workloads CoreWeave is known for: how do you ingest, store, process, and serve data at the scale and performance that next-generation AI demands? That is not a solved problem, and the team that gets it right will define how AI infrastructure is built for the next decade. I want to be the PM who owns that roadmap. What specifically draws me to this role is the combination of 0-to-1 product definition and the depth of customer co-design it requires. The JD describes diving deep into customer data architectures and co-designing solutions for large-scale deployments — that is exactly how I operated at Intuit, where I conducted an enterprise-wide Service Language Assessment across nine languages to inform CTO-level investment decisions, and built Asterias, a declarative asset lifecycle management platform with a GraphQL API, based on telemetry and usage data from 20 mobile apps and 30+ product SKUs. I am comfortable going deep technically with customers and translating that into roadmap clarity. ## Selected Relevant Experience - **ICE platform scale:** Achieved 275% YoY growth in ICE engagements, scaling to 675M+ in FY23; drove rSocket migration from 6K to 50K TPS supporting ~1.5M concurrent connections at sub-25ms TP99. - **Developer platform 0-to-1:** Delivered ICE Self-Service platform (DevPortal, GitOps config, ICE Playground), reducing developer onboarding from 2–3 weeks to minutes in pre-prod and <24 hours for production, mitigating $1M+ in projected opex growth. - **SDK and API-first platform delivery:** Extended Java and Python SDK Starter Kits with scaffolding templates, build configurations (Gradle/Maven), testing frameworks, and CI/CD integration — enabling developers to go from zero to production-ready microservice in minutes. - **Streaming and data infrastructure:** Owned Search Service (Go microservices) and Search Catalog (PostgreSQL metadata service) at Splunk; led query performance optimization achieving up to 10x improvements in SCS search for enterprise customers. - **High-volume data operations:** Led enterprise rollout of Splunk Logging-as-a-Service (1.7 TB daily volume, 200+ internal customers); built Redis/XC10 caching layer for scalability, fault tolerance, and data redundancy at Kaiser Permanente. - **AI/ML data pipeline experience:** Built RAG retrieval pipeline with ChromaDB vector store, multi-provider LLM orchestration with fallback routing, and token budget optimization at Fintellect AI; built aeval evaluation platform on FastAPI, TimescaleDB, and Redis with CI/CD regression detection. - **RL workbench on GPU infrastructure:** Built post-training RL platform benchmarking 12 algorithms across TRL, VeRL, OpenRLHF, and NeMo RL with GPU Docker passthrough and live SSE metric streaming — direct experience with the workloads CoreWeave's customers run. - **Drift detection and configuration governance:** Initiated MSaaS Drift Detection program at Intuit — wrote Java JAR library to scan Git repos for configuration drift and built remediation roadmap using OpenRewrite, a precursor to the kind of data governance and catalog work this role requires. ## Closing CoreWeave's mission — turning compute into capability for the pioneers building the next generation of AI — is one I find genuinely compelling, and the data services layer is where that mission gets operationalized at scale. I have spent 12 years building the kind of infrastructure that other teams depend on, and I know what it takes to ship platform products that earn that trust. I would welcome the opportunity to discuss how my background maps to what you are building. Thank you for your consideration. --- **O. Felix Amoruwa** famoruwa@berkeley.edu | 909-731-9011 | felixamoruwa.info