Research Scientist (Generative Modeling)

World Labs•San Francisco, CA

About The Position

World Labs is a frontier AI research and product company advancing spatial intelligence, the next frontier beyond large language models. Co-founded by Dr. Fei-Fei Li, Justin Johnson and Ben Mildenhall, the company is pioneering world models that perceive, generate, reason, and interact with virtual and physical worlds. The company’s flagship product, Marble, transforms text, images, and video into fully navigable 3D worlds, unlocking applications across gaming, film, architecture, robotics, and immersive digital experiences. Backed by leading investors and with over $1B raised, World Labs is assembling a world-class team at the intersection of AI research and real-world deployment. We are looking for a talented Research Scientist with a strong background in generative modeling, particularly diffusion models, to join our modeling team. This role is ideal for candidates with deep expertise in diffusion models applied to images, videos, or 3D assets and scenes. While not required, experience in one or more of the following areas is a strong plus: 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. You will collaborate closely with researchers, engineers, and product teams to bring advanced 3D modeling and machine learning techniques into real-world applications, ensuring that our technology remains at the forefront of visual innovation. This role involves significant hands-on research and engineering work, driving projects from conceptualization through to production deployment.

Requirements

3+ years of experience in generative modeling or applied ML roles, ideally at a startup or other fast-paced research environment
Extensive experience with machine learning frameworks such as PyTorch or TensorFlow, especially in the context of diffusion models and other generative models
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 history of publications or open-source contributions involving large-scale diffusion models
Strong coding proficiency in Python and experience with GPU-accelerated computing.
Ability to engage effectively with researchers and cross-functional teams, clearly translating complex technical ideas into actionable tasks and outcomes.
Comfortable operating within a dynamic startup environment with high levels of ambiguity, ownership, and innovation.

Nice To Haves

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
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

Responsibilities

Design, implement, and train large-scale diffusion models for generating 3D worlds
Develop and experiment with large-scale diffusion models to add novel control signals, adapt to target aesthetic preferences, or distill for efficient inference
Collaborate closely with research and product teams to understand and translate product requirements into effective technical roadmaps.
Contribute hands-on to all stages of model development including data curation, experimentation, evaluation, and deployment.
Continuously explore and integrate cutting-edge research in diffusion and generative AI more broadly
Act as a key technical resource within the team, mentoring colleagues, and driving best practices in generative modeling and ML engineering