cover_letter / art_QVtG2Ph3oU8

Dear Lyft Verticals Hiring Team, Lyft sits at the intersection of infrastructure and human experience — the moment someone lands at SFO after a red-eye, the rider who pre-books a 5 AM airport run, the crowd streaming out of Chase Center after a Warriors game. These are not generic transportation moments; they are high-stakes, time-sensitive, and emotionally loaded. That specificity is exactly what drew me to this role. My background building and scaling complex marketplace platforms — from developer infrastructure serving 675M+ engagements at Intuit to 0-to-1 AI products with real-time logistics pipelines — has trained me to think precisely about the operational and product challenges that define Lyft's Verticals. **Technical and Product Foundation** My product instincts are grounded in engineering. At Intuit, I owned the ICE platform — a real-time microservices infrastructure supporting ~1.5M concurrent connections across QuickBooks, TurboTax, Mint, Mailchimp, and Credit Karma. Scaling throughput from 6K to 50K TPS via rSocket migration, achieving 275% YoY engagement growth to 675M+ FY23 engagements, and reducing developer onboarding from weeks to minutes required exactly the kind of cross-functional coordination — engineering, data science, design, operations — that the Verticals GPM role demands. I worked directly with telemetry and SQL/BigQuery usage data to surface developer pain points across ~20 mobile apps and 30+ product SKUs, building prioritization frameworks that balanced internal partner needs, third-party developer requirements, and Fortune 500 customer demands. At Splunk, I owned Search Service (Go microservices), Search Catalog, and SPL/SPL2 — delivering the Scheduler Service end-to-end in four months and leading a query performance initiative that achieved up to 10x improvements for a beta enterprise customer. I designed a repeatable RICE-based prioritization framework across three microservice backlogs, balancing competing stakeholder demands under tight timelines. These experiences gave me a durable framework for managing product complexity in fast-moving, operationally intensive environments. Most recently, as founder of Streamio AI and Fintellect AI, I have shipped production consumer applications end-to-end — from architecture through App Store approval — including real-time HLS livestreaming pipelines, multi-agent orchestration systems, and RAG retrieval pipelines with multi-provider LLM fallback routing. Building and launching products without an organizational safety net has sharpened my judgment about what matters, what to cut, and how to move. **Why This Role, Why Now** The Verticals team's mandate — airports, scheduled rides, events — maps directly onto the product challenges I find most compelling: dynamic supply-demand matching under high variability, demand forecasting in time-sensitive corridors, and designing incentive structures that balance rider confidence with driver predictability. These are marketplace dynamics problems with real human stakes, and they require the same blend of quantitative rigor and customer empathy I applied at Intuit when scaling ICE to serve millions of concurrent users. **Role-Specific Connection** The airport vertical in particular resonates with me. Airports are operationally complex in ways that expose every weakness in a matching and routing system — flight delays, surge conditions, geofenced pickup zones, and riders who have zero tolerance for friction after a long flight. The opportunity to redesign that end-to-end experience, from pre-booking through curbside pickup, while simultaneously improving real-time driver guidance and earnings predictability, is a genuinely hard product problem. I am also drawn to the events vertical: precision logistics at stadium scale, where demand is known in advance but execution windows are narrow, is a forecasting and incentive design challenge I would approach with the same data-driven rigor I applied to capacity planning and demand forecasting at Kaiser Permanente across multiple datacenters. **Selected Relevant Experience** - **675M+ platform engagements, 275% YoY growth (Intuit):** Scaled ICE platform throughput from 6K to 50K TPS via rSocket migration supporting ~1.5M concurrent connections with sub-25ms TP99 — directly analogous to the real-time matching and reliability demands of Lyft's high-variability verticals. - **Developer onboarding from weeks to minutes (Intuit):** Delivered ICE Self-Service platform (DevPortal, GitOps config, ICE Playground), reducing onboarding from 2–3 weeks to minutes in pre-prod and <24 hours for production, while mitigating $1M+ in projected opex growth. - **$480K/month incremental revenue via ICE Presence (Intuit):** Identified and shipped async chat presence feature, deployed Background-to-Foreground Messaging on iOS/Android with <100ms latency — demonstrating the ability to connect platform investment to measurable business outcomes. - **10x query performance improvement (Splunk):** Led benchmark initiative for enterprise beta customer, building mirrored topology for performance testing — the same methodology applies to A/B experimentation and matching algorithm optimization in rideshare. - **Scheduler Service delivered in ~4 months (Splunk):** End-to-end ownership from PRD through .conf19 demo, coordinating engineering, design, and partner teams under a fixed delivery window. - **Demand forecasting and capacity planning (Kaiser Permanente):** Led enterprise-wide capacity planning for SOA product suite across multiple datacenters, analyzing demand drivers and developing IT capacity plans — foundational skills for Lyft's events and scheduled rides forecasting challenges. - **0-to-1 consumer product launches (Streamio AI / Fintellect AI):** Shipped production iOS, macOS, and web applications from prototype through App Store approval, including real-time data pipelines, multi-agent orchestration, and subscription monetization — demonstrating full product ownership across the entire lifecycle. - **Enterprise-wide language and platform assessment (Intuit):** Conducted Service Language Assessment across 9 languages, analyzing usage data and developer feedback to inform strategic investment decisions presented to CTO — the kind of structured analytical framing required for executive communication at Lyft. **Closing** Lyft's purpose — to serve and connect — is not abstract to me. Transportation access shapes how people live, work, and participate in their communities. The Verticals team owns the moments where that purpose is most visible and most tested: the airport pickup that sets the tone for a trip, the scheduled ride that gives a rider confidence on their most important day, the post-game surge that either earns driver loyalty or loses it. I would bring to this role the same ownership mindset, data-driven prioritization, and cross-functional execution discipline that scaled platforms to hundreds of millions of engagements — applied now to the product challenges that matter most to Lyft's riders, drivers, and long-term business. I would welcome the opportunity to discuss how my background maps to the specific challenges your team is navigating. Sincerely, **O. Felix Amoruwa** famoruwa@berkeley.edu | 909-731-9011 | felixamoruwa.info