Role Overview

We are seeking a talented Research Scientist specializing in generative modeling and diffusion models to join our modeling team. This role is ideal for someone who is an expert at pre-training or post-training of large-scale diffusion models for images, videos, or 3D assets or scenes.

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.

Key Responsibilities

• Design, implement, and train large-scale diffusion models for generating 3D worlds

• Develop and experiment with post-training for 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

Ideal Candidate Profile

• 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, 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 Have

• 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