cover_letter / art_NKVZ_HOKb4o

Dear CoreWeave Hiring Team, CoreWeave is building the essential infrastructure layer for the AI era — GPU-dense, purpose-built, and operating at a scale that frontier labs like OpenAI and Microsoft depend on. That mission resonates with me directly: I built my RL post-training workbench on GPU clusters, benchmarking GRPO and DPO across TRL, VeRL, OpenRLHF, and NeMo RL, and I understand firsthand what it means when infrastructure fails a researcher mid-run. The difference between a well-instrumented data center platform and a poorly managed one is measured in lost GPU-hours, missed deadlines, and eroded trust — and I want to help CoreWeave get that right at scale. **Technical and Platform Foundation** My product career has been anchored in platform infrastructure — the systems that developers, operators, and engineers depend on to do their work. At Intuit, I owned the ICE platform end-to-end: developer onboarding, SDK scaffolding, telemetry strategy, and the underlying infrastructure that scaled from 6K to 50K TPS via rSocket migration, supporting approximately 1.5M concurrent connections at sub-25ms TP99 latency. That work required the same disciplines this role demands — data modeling, system-of-record decisions, cross-functional alignment across engineering and operations, and a relentless focus on uptime and reliability. Achieving 275% YoY growth in ICE engagements to 675M+ in FY23 across QuickBooks, TurboTax, Mint, Mailchimp, and Credit Karma was not a marketing outcome; it was an infrastructure and platform execution outcome. At Splunk, I owned Search Service (Go microservices), Search Catalog (PostgreSQL metadata service), and Splunk Processing Language — products that are fundamentally about telemetry, observability, and making operational data actionable. I led a query performance optimization initiative that achieved up to 10x improvements in Splunk Cloud Services search for a beta enterprise customer, building a mirrored topology specifically for benchmark testing. That experience maps directly to the KPI and telemetry strategy work this role requires: defining data models, establishing coverage metrics, and translating raw operational signals into actionable dashboards for both technicians and leadership. On the AI and data side, I built aeval, a local-first model evaluation platform with a FastAPI orchestrator, TimescaleDB for time-series metrics, Redis job queuing, and statistical rigor including bootstrap confidence intervals, Welch's t-test, and Cohen's d effect size. I also built an automated visual evaluation system (AutoEval) that reduced robot model evaluation cycles from 72 hours to approximately 4 minutes by repurposing a multimodal AI pipeline for spatial reasoning on prediction frames. These projects reflect the same instinct this role calls for: embed data and AI into operational workflows rather than bolting them on as afterthoughts. **Why This Role** The Senior PM, Data Center role at CoreWeave sits at the intersection of infrastructure operations, data strategy, and AI-driven automation — a combination I have been building toward across my career. What specifically draws me to this role is the scope: owning the full technology ecosystem across DCIM, CMMS/EAM, BMS/SPoG, construction management, and asset lifecycle for a rapidly expanding multi-site portfolio. CoreWeave's Blackwell GPU cluster buildout and its expanding US and European footprint mean this is not a maintenance role — it is a 0-to-1 platform build at hyperscale, with real operational stakes. The opportunity to define digital twin capabilities, drive telemetry coverage across the portfolio, and embed AI into technician and leadership workflows is exactly the kind of product challenge I find most meaningful. **Selected Relevant Experience** - **ICE Self-Service Platform (Intuit):** Delivered DevPortal, GitOps config, and ICE Playground, reducing developer onboarding from 2–3 weeks to minutes in pre-production and under 24 hours for production, while mitigating $1M+ in projected opex growth — a direct analog to the site onboarding standardization and playbook work this role requires. - **Infrastructure Scaling (Intuit):** Scaled ICE throughput from 6K to 50K TPS via rSocket migration supporting ~1.5M concurrent connections at sub-25ms TP99 — demonstrating fluency in high-performance infrastructure requirements and capacity planning. - **Asterias Asset Lifecycle Platform (Intuit):** Built a declarative asset lifecycle management platform with a GraphQL API, informed by telemetry and usage data across ~20 mobile apps and 30+ product SKUs — directly relevant to asset lifecycle and CMMS/EAM product ownership. - **MSaaS Drift Detection Program (Intuit):** Initiated and led a configuration drift detection and remediation program: wrote a Java JAR library to scan Git repos for drift, partnered with Design on DevPortal UI, and built a remediation roadmap using OpenRewrite — a strong analog to maintaining system-of-record integrity across a distributed data center portfolio. - **Search Service and Telemetry (Splunk):** Owned Go microservices, PostgreSQL metadata catalog, and SPL/SPL2 for Splunk Cloud Services; led benchmark testing that achieved up to 10x query performance improvements — establishing credibility in observability, data modeling, and analytics platform work. - **Splunk Logging-as-a-Service (Kaiser Permanente):** Led development and enterprise rollout of a Splunk Logging-as-a-Service platform handling 1.7 TB daily volume across 200+ internal enterprise customers, plus ITSI Application Monitoring-as-a-Service — direct experience with large-scale operational telemetry platforms in a mission-critical environment. - **RL Workbench (AI/ML Research):** Built a GPU-backed post-training platform with Docker GPU passthrough, live SSE metric streaming, and head-to-head framework benchmarking across TRL, VeRL, OpenRLHF, and NeMo RL — giving me firsthand experience as a CoreWeave customer use case and a concrete understanding of what GPU infrastructure reliability means to practitioners. **Closing** CoreWeave's mission — turning compute into capability for the pioneers building the next generation of AI — depends on data center operations that are instrumented, predictable, and continuously improving. I have spent 12 years building the platforms that make complex infrastructure legible and operable: for developers at Intuit, for enterprise search at Splunk, for operational monitoring at Kaiser Permanente, and for AI practitioners in my own research work. I would bring that same discipline to defining CoreWeave's data center technology ecosystem, and I would be glad to discuss how my background maps to your specific platform priorities. Thank you for your consideration. --- **O. Felix Amoruwa** famoruwa@berkeley.edu | 909-731-9011 | felixamoruwa.info