cover_letter / art_h4YhpWtn6Jk

Dear Brex Hiring Team, Brex is building what the best finance teams actually need: a unified financial OS that eliminates the manual overhead between spending, visibility, and control — and does it at global scale across 200+ markets. That mission sits at the intersection of infrastructure and intelligence in a way that few fintech companies have executed credibly. My path from hand-coding backpropagation through time in C++ at Berkeley, to scaling Intuit's platform to 675M+ engagements, to building AI-native fintech products as a founder, has been oriented toward exactly this kind of high-leverage, technically grounded product work. ## Technical and AI Foundation My technical credibility is not advisory — it is hands-on and current. In 2025–2026, I built a full RL post-training workbench covering the RLHF/DPO pipeline end-to-end: a Reward Lab for designing and A/B testing reward functions across GSM8K, MATH, HumanEval, and UltraFeedback; a Playground running real TRL-powered GRPO and DPO training with live SSE metric streaming on Apple Silicon and CUDA; and an Arena for head-to-head benchmarking of TRL, VeRL, OpenRLHF, and NeMo RL with GPU passthrough in Docker containers. I implemented 12 RL algorithms with standardized throughput, memory, and convergence benchmarking across frameworks. This is not a survey — it is a working system. On the evaluation 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 — with CI/CD integration and automated safety gates. The stack: FastAPI orchestrator, TimescaleDB, Redis job queue, Next.js dashboard, Ollama. These projects reflect how I think about AI systems: not as black boxes to integrate, but as infrastructure to instrument, benchmark, and improve. My NeurIPS 2014 paper on neural networks for protein secondary structure prediction — and the 2026 rewrite of that original C++ system into a PyTorch platform spanning 413 parameters to 8B (a 19-million-fold scale increase) — establishes a research foundation that informs how I reason about model architecture, training dynamics, and evaluation rigor. ## Connecting the Arc The through-line across my career is building platforms that abstract complexity for the people who depend on them — whether those people are developers, finance teams, or retail investors — and doing so with the technical depth to make the right architectural tradeoffs, not just the expedient ones. Brex's move upmarket into enterprise, combined with its AI-native automation layer, is exactly the kind of inflection point where that combination of platform thinking and technical fluency creates durable advantage. ## Why This Role What draws me specifically to the Staff PM role at Brex is the mandate to define multi-quarter strategy across a critical product domain — financial infrastructure, risk systems, or the payables ecosystem — while engaging meaningfully in architectural discussions with Engineering. Brex's AI-powered expense categorization and policy enforcement are the kinds of systems where the product decisions and the technical decisions are inseparable: the data model determines what the AI can learn, the experimentation framework determines how fast you can improve it, and the compliance constraints shape what you can ship. I want to work in that space, at that level of integration. ## Selected Prior Experience - **Intuit — ICE Platform at scale:** Achieved 275% YoY growth in ICE engagements, scaling to 675M+ in FY23 across QuickBooks, TurboTax, Mint, Mailchimp, and Credit Karma; scaled throughput from 6K to 50K TPS via rSocket migration supporting ~1.5M concurrent connections with sub-25ms TP99. - **Intuit — Developer onboarding infrastructure:** Delivered ICE Self-Service platform (DevPortal, GitOps config, 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. - **Intuit — Data-driven prioritization:** Worked closely with telemetry and usage data (SQL, 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. - **Fintellect AI — AI fintech platform:** Architected RAG retrieval pipeline with ChromaDB vector store, multi-provider LLM orchestration (Claude, GPT-4, Gemini) with fallback routing, structured output validation, and token budget optimization — deployed in a mobile-first financial education and investing platform for retail investors. - **Splunk — Search infrastructure PM:** Owned Search Service (Go microservices), Search Catalog (PostgreSQL metadata service), and Splunk Processing Language (SPL/SPL2); delivered Scheduler Service end-to-end in approximately four months and achieved up to 10x query performance improvements for a beta Fortune 500 customer. - **Kaiser Permanente — Platform at enterprise scale:** Led development and enterprise rollout of Splunk Logging-as-a-Service handling 1.7 TB daily volume across 200+ internal enterprise customers; built caching capability using Redis and XC10 to address scalability, fault tolerance, and data redundancy. - **Intuit — Revenue-generating platform feature:** Implemented ICE Presence in async chat, generating $480K/month in additional invoicing; deployed Background-to-Foreground Messaging on iOS/Android with sub-100ms latency. ## Closing Brex's mission — enabling companies to spend smarter and move faster — is most meaningful when the underlying platform is rigorous enough that finance teams can trust it completely and fast enough that it doesn't slow them down. That requires product leadership that can hold both the strategic frame and the technical detail simultaneously. That is the work I have been doing, in different forms, for twelve years. I would welcome the opportunity to bring that to Brex. Thank you for your consideration. --- **O. Felix Amoruwa** famoruwa@berkeley.edu | 909-731-9011 | felixamoruwa.info