ironquill.tech/board

$ cat jobs/ai-research-engineer-tether-a31b790183c9.json

AI Research Engineer

Tether·Worldwide·Anywhere in the World·mid
rails
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<img src="https://we-work-remotely.imgix.net/logos/0171/4688/logo.gif?ixlib=rails-4.0.0&w=50&h=50&dpr=2&fit=fill&auto=compress" /> <p> <strong>Headquarters:</strong> El Salvador <br /><strong>URL:</strong> <a href="https://careers.tether.io/">https://careers.tether.io/</a> </p> <div class="sc-1fwbcuw-0 kbZOmE"> <div> <p><strong><em>Why Join Us?</em></strong></p> <p>Our team is a global talent powerhouse, working remotely from every corner of the world. If you’re passionate about making a mark in the fintech space, this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards. We’ve grown fast, stayed lean, and secured our place as a leader in the industry.</p> <p>If you have excellent English communication skills and are ready to contribute to the most innovative platform on the planet, Tether is the place for you.</p> <p><strong>Are you ready to be part of the future?</strong></p> <p> </p> <p><strong>About the job</strong></p> <p>As a member of the AI model team, you will drive innovation in reinforcement learning approaches for advanced models. Your work will optimize decision-making and adaptive behavior to deliver enhanced intelligence, improved performance, and domain-specific capabilities for real-world challenges. You will work across a broad spectrum of systems, including resource-efficient models designed for limited hardware environments and complex multi-modal architectures that integrate data such as text, images, and audio.</p> <p>We expect you to have deep expertise in designing reinforcement learning systems and a strong background in advanced model architectures. You will adopt a hands-on, research-driven approach to developing, testing, and implementing novel reinforcement learning algorithms and training frameworks. Your responsibilities include curating specialized simulation environments and training datasets, strengthening baseline policy performance, and identifying as well as resolving b

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