Research Scientist (generative Modeling)

World Labs World Labs · AI Frontier · San Francisco, CA · Post-Training

Research Scientist role focused on generative modeling, specifically diffusion models for 3D worlds. Involves designing, implementing, and training large-scale models, with a focus on pre-training, post-training, and efficient inference. Collaborates with research, engineering, and product teams to bring advanced 3D modeling techniques into real-world applications.

What you'd actually do

  1. Design, implement, and train large-scale diffusion models for generating 3D worlds
  2. Develop and experiment with large-scale diffusion models to add novel control signals, adapt to target aesthetic preferences, or distill for efficient inference
  3. Collaborate closely with research and product teams to understand and translate product requirements into effective technical roadmaps.
  4. Contribute hands-on to all stages of model development including data curation, experimentation, evaluation, and deployment.
  5. Continuously explore and integrate cutting-edge research in diffusion and generative AI more broadly

Skills

Required

  • 3+ years of experience in generative modeling or applied ML roles
  • Extensive experience with machine learning frameworks such as PyTorch or TensorFlow
  • Deep expertise in at least one area of generative modeling: pre-training, post-training, diffusion distillation, fine-tuning with new conditioning signals, etc for diffusion models
  • Strong coding proficiency in Python
  • experience with GPU-accelerated computing
  • Ability to engage effectively with researchers and cross-functional teams

Nice to have

  • experience in large-scale model training
  • experience in data curation for pretraining or post-training
  • experience with tokenizers and VAEs for image, video, or 3D data
  • experience with long-context architectures
  • experience with 3D vision
  • Contributions to open-source projects in the fields of computer vision, graphics, or ML.
  • Familiarity with large-scale training infrastructure (e.g., multi-node GPU clusters, distributed training environments).
  • Experience integrating machine learning models into production environments.
  • Led or been involved with the development or training of large-scale, state-of-the-art generative models

What the JD emphasized

  • strong background in generative modeling
  • deep expertise in diffusion models
  • large-scale model training
  • data curation for pretraining or post-training
  • tokenizers and VAEs for image, video, or 3D data
  • long-context architectures
  • 3D vision
  • Strong history of publications or open-source contributions involving large-scale diffusion models

Other signals

  • diffusion models
  • 3D world models
  • spatial intelligence
  • generative AI