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Senior Machine Learning Engineer, Engine Optimization
3 weeks ago
Harmony is the critical subsystem responsible for ensuring a stable, enjoyable, and interactive experience for Roblox users across all devices. By intelligently orchestrating and adjusting presentation quality from high-fidelity rendering down to basic interaction. This role offers a unique opportunity to pioneer the application of machine learning to real-time, low-latency systems optimization.
You will define the ML strategy for resource management within the engine, moving beyond traditional heuristics to build adaptive systems. You will solve complex, high-stakes resource allocation and quality scaling problems across memory, CPU/GPU compute, battery life, and network bandwidth. We aim to leverage predictive modeling and optimization techniques to handle highly dynamic content and opaque resource signals from the OS, transforming the stability and quality of the global Roblox experience.
You Will
- Analyze massive-scale engine performance and user engagement telemetry to drive the team's strategic roadmap, using data insights to prioritize high-impact optimization targets
- Design and implement machine learning models to predict and manage critical engine resource constraints in real time.
- Develop adaptive control systems that use ML outputs to dynamically adjust engine fidelity parameters, maximizing user experience while maintaining stability and low 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 roadmap for introducing and scaling ML infrastructure within the Harmony subsystem, ensuring reliable operation at massive scale.
You Have
- Expertise in one or more areas of applied machine learning: reinforcement learning (RL) for control systems, resource scheduling, predictive modeling (especially time-series analysis for resource usage), or real-time optimization.
- Proficiency in one or more programming languages (e.g., C++, Python, Go, Java) and experience designing, building, and deploying ML models in a performance-critical environment.
- A strong understanding of low-latency, systems-level concepts (e.g., memory management, threading, operating system signals) is a significant advantage.
- A track record of solving complex optimization problems or experience integrating AI/ML into core engineering products, preferably in gaming or mobile environments.
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