Principal Machine Learning Infrastructure Engineer, Ads & Discovery

Roblox•San Mateo, CA

5d•$293,800 - $343,340•Onsite

About The Position

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. With Roblox Ads & Discovery business growing at a rapid rate, we are building large scale ads machine learning infrastructure to deliver more value to our users and our advertisers. As a Machine Learning Infrastructure Engineer, you’ll build scalable, reliable, and high-performance infrastructure that powers ML systems across our organization. You’ll operate at the scales of hundreds of billions of engagements, and redefine how we deliver performance ads to hundreds of millions of users.

Requirements

5+ years of experience designing, building, and deploying large-scale machine learning systems in production environments.
3+ years of experience tech leading ML infrastructure engineers
Strong communication skills and a collaborative approach to problem solving.
BS, MS, or Ph.D. in Computer Science, Engineering, or equivalent experience.

Responsibilities

Lead strategic planning and roadmap execution of scalable production-ready ML systems including model training, data pipelines, feature engineering and model inference.
Own the architecture, establish engineering best practices of scalability, reliability, and cost-effectiveness of ML infrastructure (e.g., training, serving, feature).
Work closely with data scientists, ML engineers, platform teams, and product stakeholders to design, implement, and operate robust ML platforms that accelerate model development and deployment.
Stay abreast of industry trends in machine learning and infrastructure to ensure the adoption of leading-edge technologies and practices.