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. 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. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. 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. ML Platform @ Roblox today supports hundreds of ML use cases and billions of inferences per day across Discovery, Safety, Engine, and much more. As a machine learning engineer on ML Platform, you will be responsible for digging deep into model internals to optimize performance, for both training and inference. We are looking for accomplished engineers to help us maximize performance of our platform. You Will: Optimize machine learning models for performance on GPU architectures, focusing on both training and inference workflows. Conduct low-level performance profiling analysis to identify bottlenecks in existing machine learning pipelines and propose actionable improvements. Contribute to the development of best practices and tooling for model optimization and deployment. Collaborate with cross-functional teams, including data scientists and software engineers, to integrate and deploy optimized models into production environments. Partner across organizations to build tooling, interfaces, and visualizations that make the ML@Roblox a delight to use. You Have: 4+ years of professional experience and a tool chest of system design experience upon which to draw to build performant systems for all of Roblox. Significant experience debugging GPUs - reading GPU profiles, debugging Xid errors, etc. Ideally have experience with model optimization techniques for LLMs, such as speculative decoding, continuous batching, quantization, etc. Bachelor's degree in Computer Science, Computer Engineering, Data Science, or a similar technical field. You Are: Proficient in advanced tools and frameworks (e.g., CUDA, Triton, TensorRT) to enhance model execution speed and reduce latency. A performance nut; you love pushing the limits of what’s possible, whether it’s squeezing every last ounce of efficiency from a GPU, fine-tuning algorithms for peak speed, or innovating new techniques to enhance model performance. A generalization advocate: you're passionate about building tools and frameworks that consistently deliver improvements in model performance. Passionate about supporting internal partners (data scientists and ML Engineers) to meet and understand their needs. For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits. Annual Salary Range:
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