cover_letter / art_PYATQL6IQPk

Dear Stripe Infrastructure Hiring Team, Stripe's mission to increase the GDP of the internet is not an abstraction to me — it is the infrastructure layer that every fintech product I have built depends on. When I architected the RAG retrieval pipeline and multi-provider LLM orchestration layer at Fintellect AI, Stripe's payments API was the foundation beneath the subscription and transaction flows powering the platform. That proximity to Stripe's infrastructure, combined with 12+ years building developer-facing platforms at scale, is what draws me to this role on the IDX team. **Technical and AI Foundation** My technical credibility spans from first principles to production systems. In 2004, I hand-coded backpropagation through time in C++ for a protein structure prediction system that became a NeurIPS 2014 accepted paper. In 2026, I rewrote that system in PyTorch across five neural architectures — feedforward, GRU, Transformer, ESM-2, and multi-task — scaling from 413 to 8 billion parameters, a 19-million-fold increase, with MLflow experiment tracking, Optuna hyperparameter optimization, and FastAPI serving across six Docker containers. That arc — from hand-rolling gradients to orchestrating modern ML infrastructure — gives me the depth to engage engineering teams on architectural trade-offs without needing translation. On the AI product 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, bootstrap confidence intervals, Welch's t-test, and Cohen's d effect size — all integrated into a CI/CD pipeline with automated safety gates. The stack runs FastAPI, TimescaleDB, Redis, and Ollama. I also built an RL post-training workbench that benchmarks GRPO and DPO across TRL, VeRL, OpenRLHF, and NeMo RL, implementing 12 RL algorithms with standardized throughput, memory, and convergence benchmarking across GPU Docker containers with passthrough. These are not survey projects — they are production-grade systems built to answer real research and product questions. At Fintellect AI, I architected a RAG retrieval pipeline with ChromaDB vector store, multi-provider LLM orchestration across Claude, GPT-4, and Gemini with fallback routing, structured output validation, and token budget optimization. At Streamio AI, I implemented the OpenClaw multi-agent orchestration framework with a gateway protocol, subagent delegation, profile management, and session switching — enabling coordinated AI agent workflows across multiple industry verticals. **Why This Role** My career has moved from enterprise platform infrastructure (Kaiser Permanente SOA, Splunk Cloud Services) to high-scale developer platforms (Intuit ICE, 675M+ engagements, 50K TPS) to founding AI-native products — a trajectory that maps directly to what the IDX team is building. Stripe's infrastructure challenges — unlocking international markets through local infrastructure, powering enterprise-grade developer experiences, and deploying internal AI agents for functions like Recruiting and Legal — are exactly the class of problems I have spent the last several years preparing to work on at this level of scale. What specifically draws me to this role is the combination of external developer experience and internal AI agent deployment. At Intuit, I delivered the ICE Self-Service platform — DevPortal, GitOps config, and ICE Playground — reducing developer onboarding from two to three weeks down to minutes in pre-production and under 24 hours for production. That same discipline of mapping customer user journeys, identifying friction, and shipping ergonomic developer tooling is what Stripe's IDX team needs as it expands its platform capabilities. The internal AI agent work — applying LLM-powered tooling to Recruiting, Legal, and other functions — mirrors what I built at Streamio with domain-scoped agents and what I architected at Fintellect with context-aware conversational agents embedded in the product surface. **Selected Relevant Experience** - **Intuit ICE Platform (675M+ engagements, 50K TPS):** Delivered ICE Self-Service platform reducing developer onboarding from weeks to minutes; scaled throughput from 6K to 50K TPS via rSocket migration supporting ~1.5M concurrent connections with sub-25ms TP99 latency — directly relevant to Stripe's high-availability distributed systems requirements. - **Developer SDK and Tooling:** Extended Java and Python SDK Starter Kits with scaffolding templates, build configurations (Gradle/Maven), testing frameworks, and CI/CD integration — enabling developers to go from zero to production-ready microservice in minutes. - **Enterprise Service Language Assessment:** Conducted enterprise-wide analysis across 9 languages for Intuit's CTO, combining SQL and BigQuery usage data with developer feedback to drive strategic infrastructure investment decisions — the kind of data-driven, cross-system analysis the IDX role requires. - **Multi-Agent AI Orchestration (OpenClaw):** Designed and implemented gateway protocol, subagent delegation, profile management, and session switching for coordinated AI agent workflows — directly applicable to Stripe's internal AI agent initiatives. - **RAG and LLM Orchestration (Fintellect AI):** Architected production RAG pipeline with ChromaDB, multi-provider LLM fallback routing, structured output validation, and token budget optimization — hands-on experience with the AI workflows called out in the JD. - **Splunk Search Orchestration:** Owned Go microservices (Search Service), PostgreSQL metadata service (Search Catalog), and SPL/SPL2 query language; delivered Scheduler Service end-to-end in four months and achieved up to 10x query performance improvements for a Fortune 500 beta customer. - **RICE Prioritization and Stakeholder Alignment:** Designed repeatable RICE-based prioritization framework for three microservice backlogs at Splunk, balancing internal partner, third-party developer, and Fortune 500 customer requirements — the same cross-functional alignment challenge the IDX team faces at Stripe's scale. **Closing** Stripe's goal of putting the global economy within everyone's reach is a mission worth the most important work of a career. The IDX team sits at the intersection of that mission and the engineering systems that make it possible — ensuring that Stripe's infrastructure meets the needs of both the engineers building on it and the businesses depending on it. I have spent 12 years building exactly at that intersection: developer platforms, AI-native products, and the data and telemetry systems that make principled prioritization possible. I would welcome the opportunity to bring that experience to Stripe. Thank you for your consideration. O. Felix Amoruwa famoruwa@berkeley.edu | 909-731-9011 | felixamoruwa.info