ML Research Engineer at Maple Finance

<h1><strong>Hi 👋 I’m Aidan, founder of Maple.</strong></h1><p style="min-height:1.5em">At <a target="_blank" rel="noopener noreferrer nofollow" href="https://maple.inc">Maple</a>, we’re building AI agents that work for restaurants. These agents answer calls, take orders, book appointments, and handle real customer interactions over natural voice.</p><p style="min-height:1.5em"></p><p style="min-height:1.5em"><strong>But our bigger mission goes deeper:</strong> we’re building <strong>automated ontologies</strong> that model how businesses <em>actually</em> operate — their services, workflows, constraints, and language — so our agents can adapt to them instantly. We meet businesses where they are, not where software wants them to be.</p><p style="min-height:1.5em"></p><p style="min-height:1.5em">We have <strong>many customers, strong revenue growth, years of runway, and backing from world-class investors</strong>. I’ll share more once we meet.</p><p style="min-height:1.5em"></p><h1><strong>About the Role</strong></h1><p style="min-height:1.5em">As an <strong>ML Research Engineer</strong> at Maple, you'll be a part of our core product team transforming cutting-edge research into production-ready voice agents, serving millions of interactions for local businesses. Collaborate with experts from Google Brain, Two Sigma, Stanford, MIT, Columbia, and IBM, rapidly deploying advanced models and systems that directly impact small businesses.</p><p style="min-height:1.5em">We work <strong>in person, 5 days a week</strong> in our NYC office. Collaboration here is fast, noisy (in the best way), and high-trust. We move quickly, break things intentionally, and fix them just as fast.</p><p style="min-height:1.5em"></p><h1><strong>What You'll Do</strong></h1><ul style="min-height:1.5em"><li><p style="min-height:1.5em">Optimize <strong>speech recognition (ASR)</strong>, <strong>large language models (LLMs)</strong>, and <strong>text-to-speech (TTS) </strong>for <strong>real-world use</strong>, ensuring accuracy in diverse, noisy environments.</p></li><li><p style="min-height:1.5em">Fine-tune LLMs with <strong>retrieval-augmented generation (RAG)</strong>, <strong>reinforcement learning (RL)</strong>, and prompt engineering for dynamic, context-aware conversations.</p></li><li><p style="min-height:1.5em">Integrate AI components into <strong>autonomous agents</strong> capable of complex tasks like scheduling, order-taking, and issue resolution.</p></li><li><p style="min-height:1.5em">Create human-in-the-loop and automated systems to monitor performance, detect anomalies, and <strong>continuously improve models from real-world feedback</strong>.</p></li><li><p style="min-height:1.5em">Develop pipelines to <strong>construct knowledge graphs from business data</strong>, powering adaptive AI interactions.</p></li><li><p style="min-height:1.5em">Work with infrastructure teams to <strong>scale models efficiently across GPU/TPU clusters</strong> and edge devices, minimizin