[2026] Senior Machine Learning Engineer, Recommendation Systems - PhD Early Career

<div class="content-intro"><p><span style="font-weight: 400;">Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. </span></p> <p><span style="font-weight: 400;">At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device.</span><strong> </strong><span style="font-weight: 400;">We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. </span></p> <p><span style="font-weight: 400;">A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone.</span></p></div><p>Recommendation Systems are a key growth lever at Roblox, driving retention, engagement, and monetization for hundreds of millions of users. This role offers the unique opportunity to redefine how users search and discover everything from the most interesting immersive experiences and digital avatars in our Marketplace to personalized advertising. You will solve a diverse range of high-scale ranking, retrieval, and personalization problems across our platform.</p> <p>We combine cutting-edge research —including deep learning, generative AI, and reinforcement learning techniques— with large-scale engineering to bridge experimentation and production; you'll design algorithms that operate at massive scale and shape the next generation of recommender systems for user-generated content.</p> <h3><strong>Teams Hiring for This Role</strong></h3> <ul> <li><strong>Search:</strong> powers major recommendation surfaces—drives user engagement by redesigning core surfaces and search/homepage ranking</li> <li><strong>Notifications:</strong> owns the distributed systems and ML platform that transform billions of Roblox signals into high‑value notifications for hundreds of millions of players. </li> <li><strong>Economy:</strong> builds the ML backbone for marketplace, monetization, and commerce (including fraud, pricing, and bundling) </li> <li><strong>Ads Brands:</strong> focuses on ranking, retrieval, and marketplace/auction theory to optimize sponsored content delivery.</li> <li><strong>Safety, Alt Defense: </strong>architects a massive-scale detection engine that identifies recidivist bad actors across billions of accounts to ensure the long-term integrity of the Roblox community.</li> </ul> <p><strong>You Will</strong></p> <ul> <li>Design and implement large-scale recommendation systems that power discovery across Roblox’s surfaces — experiences, avatars, and creator content.</li> <li>Develop deep learning models for ranking, retrieval, and personalization using approaches in multimodal models, LLMs, and generative AI.</li> <li>Collaborate with applied researchers, engineers, and product teams to advance experimentation and accelerate innovation.</li> <li>Translate research into production systems that impact hundreds of millions of daily active users.</li> <li>Work backward from user and product needs to deliver ML solutions that drive engagement, retention, and ecosystem growth.</li> </ul> <p><strong>You Have</strong></p> <ul> <li>Possessing or pursuing a PhD in computer science, engineering, or a related field, with a thesis aligned to Roblox’s research areas.</li> <li>Expertise in one or more areas: recommender systems, search systems, information retrieval, or generative models (e.g., LLMs, VLMs, VLAs)</li> <li>Ability to design and architect systems for efficient personalization and user interest modeling using advanced attention mechanisms (e.g., sparse/linear attention).</li> <li>A strong research track record, evidenced by multiple publicati ... (truncated, view full listing at source)