cover_letter / art_vzFTdNsa71A

Dear OpenAI Platform Partnerships Hiring Team, OpenAI sits at the center of the most consequential technology transition of our lifetimes — not just building frontier models, but actively shaping how the world's developers, enterprises, and end users connect to them through Codex, ChatGPT, and the broader API platform. That intersection of product, ecosystem, and commercial strategy is where I have spent the last several years: building multi-agent orchestration frameworks from scratch, shipping developer-facing platforms at scale, and closing the loop between technical architecture and go-to-market execution. The Platform Product Partnerships Lead role is a direct expression of the work I find most meaningful. **Technical and Product Foundation** My technical credibility in the agent and platform space is hands-on, not advisory. At Streamio AI, I designed and built OpenClaw — a production multi-agent orchestration framework with a gateway protocol, subagent delegation, profile management, and session switching — coordinating AI agent workflows across real estate, insurance, health, and financial-markets verticals. This is the same architectural problem OpenAI is solving at scale with Codex and the ChatGPT Operator/Agents platform: how do you expose durable, composable agent primitives that third-party developers can build on reliably? I have lived that design space from the implementation side. On the evaluation and post-training side, I built a 3-phase RL post-training workbench covering the full RLHF/DPO pipeline — implementing 12 RL algorithms (PPO, GRPO, DAPO, REINFORCE, DPO, SimPO, and others), benchmarking TRL, VeRL, OpenRLHF, and NeMo RL head-to-head with GPU Docker passthrough, and streaming live training metrics via SSE on Apple Silicon and CUDA. This gives me peer-level fluency with the post-training and alignment work OpenAI's research teams are doing — a meaningful foundation for translating partner technical constraints into credible product recommendations. My NeurIPS 2014 publication on neural networks for protein secondary structure prediction, and the subsequent 2026 rewrite of that system from 413 to 8B parameters using PyTorch, MLflow, and Optuna, establish that my AI depth predates the current wave and has continued to compound. **Why This Role, Why Now** The Platform Partnerships Lead role is precisely the seat where technical judgment, commercial deal-making, and ecosystem strategy converge — and that convergence is where I have operated across Intuit, Splunk, and my own ventures. At Intuit, I owned the developer platform strategy for ICE, scaling it to 675M+ engagements in FY23 across QuickBooks, TurboTax, Mint, Mailchimp, and Credit Karma, and drove a throughput increase from 6K to 50K TPS via rSocket migration. I also led the enterprise-wide Service Language Assessment across 9 languages, synthesizing usage data and developer feedback into strategic investment recommendations presented to the CTO — the same kind of structured, data-grounded prioritization this role demands. What specifically excites me about this position is the mandate to shape how connectors, agents, and third-party workflows function inside ChatGPT and Codex. OpenAI's recent launches — Codex on Windows, the Dell Technologies partnership for hybrid/on-premises enterprise environments, and the personal finance experience in ChatGPT — signal a platform that is actively expanding its integration surface. I want to be the person driving the partnership strategy that determines which third-party services become native to that surface, and how the commercial and technical terms make those integrations durable. **Selected Prior Experience** - **OpenClaw multi-agent orchestration (Streamio AI):** Designed and implemented a production gateway protocol with subagent delegation, profile management, and session switching — directly applicable to shaping how third-party agent workflows integrate into ChatGPT and Codex. - **ICE platform scale (Intuit):** Achieved 275% YoY growth in ICE engagements, scaling to 675M+ in FY23; drove rSocket migration supporting ~1.5M concurrent connections with sub-25ms TP99 — demonstrated ability to own platform infrastructure decisions with consumer-scale consequences. - **ICE Self-Service DevPortal (Intuit):** Delivered GitOps config, ICE Playground, and DevPortal, 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 — the kind of developer experience investment that drives ecosystem adoption. - **Fintellect AI RAG pipeline:** Architected a multi-provider LLM orchestration layer (Claude, GPT-4, Gemini) with fallback routing, structured-output validation, and token-budget optimization — direct experience with the API platform dynamics OpenAI's partners navigate. - **Splunk Scheduler Service (end-to-end delivery):** Owned and shipped Scheduler Service in approximately 4 months, enabling scheduled search for first-party applications and demoed at .conf19 — evidence of full-lifecycle product ownership under aggressive timelines. - **Java/Python SDK Starter Kits (Intuit):** Extended SDK scaffolding with build configurations, testing frameworks, and CI/CD integration, enabling developers to reach production-ready microservices in minutes — hands-on developer tooling experience relevant to Codex's SDK and integration surface. - **0-to-1 product strategy and go-to-market (Streamio AI / Fintellect AI):** Led customer discovery, iterative product refinement, App Store launches, influencer partnerships, and monetization architecture across two ventures — the full commercial and GTM motion this role requires. **Closing** OpenAI's mission — ensuring that general-purpose AI benefits all of humanity — is not background context for me; it is the reason the Platform Partnerships role carries real weight. The integrations this team builds determine which third-party services become part of how hundreds of millions of people interact with AI. Getting those partnerships right, structuring agreements that are technically sound and commercially durable, and helping the product team decide what gets built and in what order — that is the work I want to do, and the work I am prepared to do. I would welcome the opportunity to discuss how my background maps to the team's current priorities. Sincerely, **O. Felix Amoruwa** famoruwa@berkeley.edu · 909-731-9011 · felixamoruwa.info