<div class="content-intro"><p><strong>About <a href="https://himalayas.app/companies/learneo">Learneo</a><a href="https://himalayas.app/companies/learneo">Learneo</a> is a platform of builder-driven businesses, including Course Hero, CliffsNotes, LitCharts, Quillbot, Symbolab, and Scribbr, all united around a shared mission of supercharging productivity and learning for everyone. We attract and scale high growth businesses built and run by visionary entrepreneurs. Each team innovates independently but has a unique opportunity to collaborate, experiment, and grow together, and they are supported by centralized corporate operations functions, including HR, Finance and Legal.</strong></p></div><p><strong><strong>About Quillbot</strong><br>Quillbot was founded in 2017 with a mission to help students and professionals—especially those learning English—strengthen their writing. Today, we help over 56 million people around the world create great things. Whether you're writing, designing, coding, or collaborating, Quillbot is a place where anyone can create at the speed of thought. Our AI-powered tools help you think clearly, communicate effectively, and create beautifully—across every platform, in any format, at any skill level. If you're passionate about using technology to make the path from inspiration to execution more accessible, intentional, and relevant, come join us.</strong></p><p><strong><strong>Role Overview</strong>We're looking for an Applied AI Engineer to join our MLOps team and take ownership of the infrastructure that keeps our machine learning models running reliably in production. This role is essential to maintaining the uptime and performance of our ML systems as usage scales. You'll work closely with data scientists, researchers, and software engineers to bridge the gap between experimentation and production—turning research artifacts into robust, monitored, and continuously improving services. This is a hands-on opportunity to shape our on-premises MLOps practices and improve engineering across the ML stack.</strong></p><h3><strong>Responsibilities</strong></h3><ul><li><strong>Collaborate with experienced data scientists and software engineers to gain insights into building scalable and efficient data pipelines, model training, and deployment systems. </strong></li><li><strong>Troubleshoot issues in the entire machine learning infrastructure, from Linux, Docker, and Kubernetes up to the highest levels of our ML stack. Resolve issues, improve system performance, and make our stack the best in the industry. </strong></li><li><strong>Assist in the design and development of on-premises MLOps solutions to support the delivery of machine learning models, and a seamless handover between research and productionization of ML artifacts </strong></li><li><strong>Drive and uphold high engineering standards, bringing consistency to codebases encountered and ensuring software is adequately reviewed, tested, and integrated. </strong></li><li><st