Senior Machine Learning Engineer, 3D Data
Roblox · San Mateo, CA, United States
About this role
## About Roblox Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences—built 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 they can imagine to life. Our vision is to reimagine how people come together—from anywhere in the world, and on any device. A career at Roblox means shaping the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. --- ## Role: Senior Machine Learning Engineer, 3D Data As a **Senior Software Engineer** on the **Foundation AI** organization, you’ll sit at the epicenter of our foundation model efforts. While the research world focuses on architecture, you’ll be the architect of the **data flywheel** that makes **VideoGen** and **3DGen** possible. You won’t just build pipelines—you’ll build the infrastructure that defines how our models perceive and generate virtual worlds in three dimensions and across time. You’ll partner directly with AI researchers to move beyond experimental datasets into **dynamic, high-fidelity data synthesis and evaluation**, bridging the gap from local research prototypes to scaling for millions of users. --- ## What you’ll do - **High-Scale Data Orchestration:** Architect and maintain automated pipelines for ingestion, cleaning, and pre-processing of multi-modal datasets (video, 3D) spanning **petabytes** of data. - **Synthetic Data Generation:** Use image and video generation models to scale multi-modal synthetic datasets. - **Research-to-Production Bridge:** Partner with research teams to create training data for research experiments—research and implement synthetic data creation pipelines. - **Scalable Evaluation Frameworks:** Build and own evaluation automation, including heuristic-based metrics and human-in-the-loop interfaces to evaluate and benchmark training datasets and in-house foundation models.
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