[2026] Senior Machine Learning Engineer, Engine Optimization - 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>Our engine’s resource management and streaming systems are critical to delivering a smooth, stable, and responsive experience for Roblox users across a huge range of devices and network conditions. These systems work together to intelligently allocate compute, memory, bandwidth, and rendering quality while dynamically delivering world content in real time as players move, explore, and interact. The challenges span highly dynamic environments, unpredictable user behavior, and opaque signals from device and OS constraints.</p> <p>This role offers a rare opportunity to pioneer the application of machine learning in real-time engine optimization. You will establish the ML framework for predictive resource allocation and content fetching, replacing heuristic-based logic with adaptive, data-driven decision-making. Your work will directly shape stability, visual quality, responsiveness, and content delivery across billions of global play sessions.</p> <p> </p> <p>You Will</p> <ul> <li data-stringify-indent="0" data-stringify-border="0">Analyze massive-scale engine performance, streaming patterns, and user behavior telemetry to uncover optimization opportunities and guide the long-term ML roadmap.</li> <li>Design ML models that infer player and interaction patterns for predictive resource management and content delivery.</li> <li>Build adaptive control systems that translate ML outputs into real-time adjustments of fidelity and system decisions, ensuring high-quality experiences without compromising stability or latency.</li> <li>Collaborate with core engine and performance engineering teams to integrate ML solutions directly into the critical path of gameplay across multiple platforms.</li> <li>Define the architectural strategy for deploying and scaling ML across resource management and streaming components at massive global scale.</li> </ul> <p> </p> <p>You Have</p> <ul> <li>Strong expertise in applied ML—such as reinforcement learning for control, predictive modeling (especially time-series and intent inference), trajectory prediction, or real-time optimization.</li> <li>Proficiency in C++, Python, Go, Java, or similar languages, with experience deploying ML models in performance-critical systems.</li> <li>A solid understanding of systems-level concepts (memory management, threading, OS signals) or a deep interest in learning them.</li> <li>A track record of solving complex optimization problems or integrating ML into real-time systems, ideally in gaming, simulation, robotics, or mobile environments</li> </ul> <p> </p> <p>Apply now to be considered for anticipated positions.</p> <p>You may redact age, date of birth, and dates of attendance/graduation from your resume if you prefer.</p> <p> </p> <p>As you apply, you can find more information about our process by signing up for Speak_. You'll gain access to our practice assessment, comprehensive guides, FAQs, and modules designed to help you ace the hiring process.</p><div class="content-pay-t ... (truncated, view full listing at source)