cover_letter / art_x1hLfJM80pc

Dear Adyen Hiring Team, Adyen occupies a rare position in financial technology: a single platform that handles payments, data, and financial products for merchants at the scale of Meta, Uber, and Microsoft. What draws me to this role is not the breadth of that platform alone, but the specific challenge of making it testable — giving merchants the confidence to go live faster, with higher-quality integrations. That problem resonates directly with work I led at Intuit, where I reduced developer onboarding from 2–3 weeks to minutes by building self-service infrastructure that removed friction between intent and production. **Technical and AI Foundation** My technical credibility in this space runs from systems-level engineering to applied AI. At Intuit, I owned the ICE Self-Service platform — DevPortal, GitOps configuration, and ICE Playground — and scaled it to 675M+ engagements in FY23 across QuickBooks, TurboTax, Mint, Mailchimp, and Credit Karma. That required understanding the full developer journey: from SDK scaffolding and CI/CD integration to telemetry-driven prioritization across 20+ mobile apps and 30+ product SKUs. I also led a Mailchimp GCP-to-AWS migration, delivered Java SDK Starter Kits with Gradle/Maven build configurations and testing frameworks, and initiated a drift detection program using a custom Java JAR library to scan Git repos for configuration drift — exactly the kind of infrastructure hygiene that underpins a reliable testing platform. On the AI side, I built **aeval**, a local-first model evaluation platform with five core eval types (factuality, reasoning, instruction-following, safety, code generation), adversarial safety testing with refusal detection, and statistical rigor via bootstrap confidence intervals, Welch's t-test, and Cohen's d effect size. The stack — FastAPI orchestrator, TimescaleDB, Redis job queue, Next.js dashboard, Ollama — was designed for CI/CD integration with automated regression detection and safety gates. I also built **AutoEval**, a visual evaluation system for robot model training that repurposed a screen capture and multimodal AI pipeline to score model outputs against natural-language rubrics, reducing evaluation cycles from 72 hours to approximately 4 minutes. These projects reflect a consistent pattern: building evaluation infrastructure that is rigorous, automated, and machine-readable — precisely what Adyen's AI-native testing vision requires. My RL Workbench further demonstrates comfort at the frontier: a 3-phase post-training platform implementing 12 RL algorithms (PPO, GRPO, DPO, and others) with live SSE metric streaming, cross-framework benchmarking across TRL, VeRL, OpenRLHF, and NeMo RL, and GPU Docker passthrough. The question "how would an LLM test a payment integration?" is one I find genuinely interesting — and one I have the technical foundation to answer in product terms. **Why This Role** The arc of my career — from platform infrastructure at Intuit to AI evaluation tooling as a founder — maps directly onto what Adyen is building: a unified, self-service testing suite that serves both human developers and AI coding agents. The challenge of consolidating a fragmented testing landscape into a coherent platform, while expanding coverage from payments simulation to financial products, Uplift, Loyalty, and Giving, is the kind of 0-to-1 platform problem I have repeatedly tackled and shipped. What specifically excites me about this role is the payments simulation domain. Modeling hundreds of payment methods across global markets — each with unique flows, edge cases, and regional behaviors — is a coverage and fidelity problem that requires the same analytical rigor I applied when conducting Intuit's enterprise-wide Service Language Assessment across nine languages, synthesizing usage data and developer feedback into strategic investment decisions presented to the CTO. I am also drawn to the AI-native testing mandate: ensuring the platform is machine-readable and developer-tooling friendly is not a future aspiration for me — it is how I have been building evaluation infrastructure for the past two years. **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-prod and under 24 hours for production, while mitigating $1M+ in projected opex growth. - **SDK Starter Kits (Intuit):** Extended Java and Python SDKs with scaffolding templates, build configurations (Gradle/Maven), testing frameworks, and CI/CD integration — enabling developers to reach production-ready microservices in minutes. - **ICE Scale (Intuit):** Achieved 275% YoY growth in ICE engagements, scaling to 675M+ in FY23; scaled throughput from 6K to 50K TPS via rSocket migration supporting ~1.5M concurrent connections with sub-25ms TP99. - **aeval (2025–2026):** Built local-first AI model evaluation platform with adversarial safety testing, statistical rigor (bootstrap CI, Welch's t-test, Cohen's d), and CI/CD integration with automated regression detection and safety gates. - **AutoEval (2025):** Reduced robot model evaluation cycles from 72 hours to ~4 minutes using a zero-integration screen capture and multimodal AI pipeline with structured PASS/FAIL reports and confidence scoring. - **MSaaS Drift Detection (Intuit):** Wrote Java JAR library to scan Git repos for configuration drift; partnered with Design on DevPortal UI and built remediation roadmap using OpenRewrite. - **Splunk Scheduler Service:** Delivered end-to-end scheduled search capability in approximately four months, enabling first-party application scheduling for Splunk Cloud Services; demoed at .conf19. - **Telemetry-Driven Prioritization (Intuit):** Used SQL and BigQuery to prioritize developer pain points across ~20 mobile apps and 30+ product SKUs; built Asterias, a declarative asset lifecycle management platform with GraphQL API. **Closing** Adyen's mission — engineering for ambition, at the scale of the world's most demanding merchants — requires a testing platform that matches that ambition. I want to build the infrastructure that gives every merchant, and every AI coding agent working on their behalf, the confidence to go live on Adyen faster and with fewer surprises. I would welcome the opportunity to discuss how my background in developer platform infrastructure, AI evaluation tooling, and 0-to-1 product leadership maps onto what your team is building. Thank you for your consideration. Sincerely, **O. Felix Amoruwa** famoruwa@berkeley.edu | 909-731-9011 | felixamoruwa.info