Senior Machine Learning Engineer, Genai Data

Roblox Roblox · Consumer · San Mateo, CA · Machine Learning

Senior Machine Learning Engineer focused on building and scaling the data infrastructure for generative AI models (VideoGen, 3DGen) at Roblox. This role involves high-scale data orchestration, synthetic data generation, bridging research to production, building evaluation frameworks, and optimizing inference APIs. The primary focus is on the data pipelines and infrastructure (L0) that feed foundation models, with a secondary focus on the evaluation frameworks (L5) for datasets and models.

What you'd actually do

  1. Architect and maintain automated pipelines for the ingestion, cleaning, and pre-processing of multi-modal datasets (video, 3D,) spanning petabytes of data
  2. Leverage image and video generation models to scale multi-modal synthetic datasets
  3. Partner with research teams to create training data for research experiments – research and implement synthetic data creation pipelines
  4. Build and own evaluation—automating both heuristic-based metrics and human-in-the-loop interfaces to evaluate and benchmark training datasets and in-house foundation models
  5. Design and optimize high-throughput, low-latency Inference APIs for internal and external consumer access

Skills

Required

  • 8+ years of experience as a research-focused data systems engineer
  • Expertise in building scalable ML data pipelines for both batch and real-time environments
  • Experience working with and processing very large datasets (Petabytes or more)
  • Python Proficiency
  • Experience with cloud data platforms and distributed processing technologies (e.g., Spark, Ray, Kubeflow, S3, etc.)

Nice to have

  • MLOps Experience
  • Custom Tooling Development
  • C++ Knowledge
  • Game development and digital content creation tools

What the JD emphasized

  • architect of the data flywheel
  • data flywheel
  • petabytes of data
  • massive datasets

Other signals

  • building the data flywheel that makes VideoGen and 3DGen possible
  • architect of the data flywheel
  • scaling for millions of users
  • design, implement, and scale robust, high-performance infrastructure to crawl, create, curate, store, and serve the massive datasets required for these models
  • ensure that our foundation models receive the highest quality data