Senior / Principal Model Optimization Engineer

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 Model Optimization 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.