ML Platform @ Roblox today supports hundreds of ML use cases and billions of inferences per day across Discovery, Safety, Engine, and much more. As an Infrastructure Engineer on ML Platform, you will be responsible for bootstrapping, scaling, and maintaining infrastructure for the entire ML platform. We are looking for accomplished engineers to help build the next generation of ML Ecosystem Tooling.

You Will:

• Bootstrap and maintain Kubernetes and Cloud infrastructure for ML Platform components--Serving Layer, Metadata Store, Model Registry, and Pipeline Orchestrator.
• Set technical strategy and oversee development of high scale and reliable infrastructure systems.
• Propose and implement new platform tooling to improve time to production for MLEs and Data Scientists across the full ML lifecycle.
• Work on infrastructure projects such as GPU fleet management, hybrid-cloud orchestration, and writing custom Kubernetes controllers and resources.
• Stay abreast of industry trends in machine learning and infrastructure to ensure the adoption of leading-edge technologies and practices.
• 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 scalable, reliable platforms for all of Roblox.
• Experience running and managing Kubernetes at scale – e.g. 100s-1000s of nodes, serving 100k+ QPS – and ideally have written your own Kubernetes controllers
• Bachelor's degree in Computer Science, Computer Engineering, Data Science, or a similar technical field.

You Are:

• Proficient in DevOps tooling such as Docker, Kubernetes, CI/CD systems, and bootstrapping cloud infrastructure (AWS, GCP, etc.)
• Experienced with the end-to-end ML model lifecycle such as model serving, training, model CI/CD, and GPU resources management, and have built ML platform features that are delightful to use.
• An automation advocate: you're passionate about infrastructure-as-code and automating painful manual processes.
• A reliability nut: you love digging into tricky postmortems and identifying weaknesses in complicated systems.
• Passionate about supporting internal partners (data scientists and ML Engineers) to meet and understand their needs.