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.

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.

You Will

• Analyze massive-scale engine performance, streaming patterns, and user behavior telemetry to uncover optimization opportunities and guide the long-term ML roadmap.
• Design ML models that infer player and interaction patterns for predictive resource management and content delivery.
• 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.
• Collaborate with core engine and performance engineering teams to integrate ML solutions directly into the critical path of gameplay across multiple platforms.
• Define the architectural strategy for deploying and scaling ML across resource management and streaming components at massive global scale.

You Have

• 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.
• Proficiency in C++, Python, Go, Java, or similar languages, with experience deploying ML models in performance-critical systems.
• A solid understanding of systems-level concepts (memory management, threading, OS signals) or a deep interest in learning them.
• A track record of solving complex optimization problems or integrating ML into real-time systems, ideally in gaming, simulation, robotics, or mobile environments

Apply now to be considered for anticipated positions.

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