cover_letter / art_mfQdar_45io

Dear Netflix Ads Platform Hiring Team, Netflix is rewriting what advertising means in the streaming era — building targeting infrastructure that respects the viewer experience while delivering measurable outcomes for enterprise advertisers at a scale no other CTV entrant can match. That intersection of technical depth and commercial consequence is where I have spent the last twelve years, and it is precisely why this role caught my attention. My path from building developer-facing platform infrastructure at Intuit — scaling a framework to 675M+ annual engagements — to architecting multi-agent AI systems and evaluation pipelines as a founder has consistently been about one thing: turning complex, multi-stakeholder signal environments into durable platform capabilities. **Technical and Platform Foundation** At Intuit, I owned the ICE platform — a developer framework and infrastructure layer consumed by QuickBooks, TurboTax, Mint, Mailchimp, and Credit Karma simultaneously. That meant making foundational architectural decisions (rSocket migration, GraphQL asset lifecycle APIs, GitOps configuration pipelines) that had to serve wildly different internal consumers while maintaining a coherent governance model. The throughput scaling story — 6K to 50K TPS, ~1.5M concurrent connections, sub-25ms TP99 — was not an accident; it was the product of disciplined signal normalization, taxonomy design for developer services, and cross-functional alignment across engineering, design, and CTO-level stakeholders. I also built Asterias, a declarative asset lifecycle management platform with a GraphQL API, specifically to solve the problem of how you structure and surface metadata across a sprawling, heterogeneous service landscape — a problem structurally analogous to how targeting signals need to be classified, governed, and consumed across forecasting, media planning, and campaign activation. On the data infrastructure side, I have worked directly with SQL and BigQuery to surface usage telemetry across ~20 mobile apps and 30+ product SKUs, translating raw signal data into prioritization decisions. I initiated and led the MSaaS Drift Detection program — writing a Java JAR library to scan Git repositories for configuration drift — because platform integrity at scale requires governance frameworks, not just good intentions. That same instinct for data quality and standards enforcement maps directly to what the Ads Targeting Platform needs: authoritative ownership of how signals are defined, classified, and maintained. **The Bridge to Ads Targeting** The Ads Targeting Platform role is, at its core, a platform infrastructure problem with a commercial surface area. It requires someone who can make foundational decisions about signal ingestion and taxonomy design alongside engineering and data science, while simultaneously translating those decisions into a GTM narrative that sales teams can use with enterprise advertisers and agencies. That is a specific combination — technical fluency plus commercial instinct — that I have exercised across every senior role I have held. **Why This Role at Netflix** What excites me specifically about this position is the architectural greenfield opportunity. Netflix is a new entrant in CTV advertising with a genuine chance to build targeting infrastructure that is differentiated from legacy DSP and SSP architectures — not constrained by decade-old data models. The responsibility to own signal governance, taxonomy design, and the full advertiser sales cycle from forecasting through campaign optimization, while serving as connective tissue between sales, GTM, engineering, and data science, is exactly the kind of high-ownership, high-visibility mandate I have sought out and delivered against. **Selected Prior Experience Most Relevant to This Role** - **Scaled ICE platform to 675M+ engagements in FY23** across five major product lines (QuickBooks, TurboTax, Mint, Mailchimp, Credit Karma), growing 275% YoY — demonstrating the ability to own a platform that serves multiple high-stakes commercial consumers simultaneously. - **Architected rSocket migration** supporting ~1.5M concurrent connections at sub-25ms TP99 and 50K TPS — foundational infrastructure decision made in partnership with engineering with direct business throughput impact. - **Built Asterias**, a declarative asset lifecycle management platform with GraphQL API — structured metadata and signal governance across a complex, multi-team service landscape. - **Delivered ICE Self-Service platform** (DevPortal, GitOps config, ICE Playground), reducing developer onboarding from 2–3 weeks to minutes — a workflow and UX leadership example where making sophisticated infrastructure accessible to non-expert users was the core product challenge. - **Conducted enterprise-wide Service Language Assessment** across 9 languages, synthesizing usage telemetry and developer feedback into strategic investment recommendations presented to the CTO — executive-level communication and cross-functional alignment at scale. - **Implemented ICE Presence in async chat**, generating $480K/month in additional invoicing — a direct example of translating a platform capability into a commercial outcome, the same motion required to shape GTM strategy for targeting products. - **Built RAG retrieval pipeline and multi-provider LLM orchestration** (Claude, GPT-4, Gemini) with structured output validation and token budget optimization at Fintellect AI — demonstrating current fluency with data pipeline architecture, signal normalization, and multi-model orchestration patterns that are directly relevant to audience signal ingestion and normalization challenges. **Closing** Netflix's mission is to entertain the world, and the Ads Platform is the infrastructure that makes that mission financially sustainable at scale. Building targeting systems that are both technically rigorous and commercially differentiated — systems that enterprise advertisers trust and that viewers experience as thoughtful — is work that matters. I would welcome the opportunity to bring my platform infrastructure background, data governance instincts, and cross-functional leadership experience to this team. Thank you for your consideration. --- **O. Felix Amoruwa** famoruwa@berkeley.edu | 909-731-9011 | felixamoruwa.info