# Research Scientist

**Company:** [World Labs](https://hotfix.jobs/companies/world-labs)
**Location:** San Francisco, CA
**Role:** AI Research
**Salary:** $250k – $325k/yr
**Experience:** 3+ years
**Skills:** Generative Modeling, Diffusion Models, 3D Vision, PyTorch, TensorFlow, Python, Gpu Computing, Computer Vision, Distributed Training, Machine Learning, Data Curation, Tokenizers, Vaes, Long-Context Architectures, 3D Modeling
**Posted:** 2025-12-29

> Research Scientist developing and deploying large-scale diffusion and generative models for 3D worlds. Requires at least 3 years of generative modeling or applied ML experience, strong Python and deep learning framework expertise, and a record of impactful research or open-source contributions.

## Job Description

## Responsibilities
- Design, implement, and train large-scale diffusion models for generating 3D worlds.
- Experiment with diffusion models to add control signals, adapt models to target aesthetic preferences, and distill models for efficient inference.
- Translate product requirements into technical roadmaps in collaboration with research and product teams.
- Contribute hands-on across data curation, experimentation, evaluation, deployment, and production integration.
- Explore and integrate advances in diffusion and generative AI.
- Mentor colleagues and drive best practices in generative modeling and machine learning engineering.

## Requirements
- 3+ years of experience in generative modeling or applied machine learning roles.
- Extensive experience with PyTorch or TensorFlow, particularly diffusion and other generative models.
- Deep expertise in an area such as pre-training, post-training, diffusion distillation, or fine-tuning with new conditioning signals.
- Strong record of publications or open-source contributions involving large-scale diffusion models.
- Strong Python programming skills and experience with GPU-accelerated computing.
- Ability to communicate complex technical concepts and collaborate effectively with researchers and cross-functional teams.
- Comfort working in a dynamic, ambiguous, high-ownership startup environment.

## Nice-to-haves
- Experience with large-scale model training.
- Data curation for pre-training or post-training.
- Tokenizers and VAEs for image, video, or 3D data.
- Long-context architectures.
- 3D vision.
- Open-source contributions in computer vision, graphics, or machine learning.
- Familiarity with multi-node GPU clusters and distributed training environments.
- Experience integrating machine learning models into production.
- Experience developing or training large-scale, state-of-the-art generative models.

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