Machine Learning Researcher, Diffusion

We are a well funded, artificial intelligence research neolab based in Toronto, Canada.

You will explore frontiers in continual learning, world modelling, and reinforcement learning on diffusion models. We love novel, provocative, untested ideas that challenge conventional paradigms. We encourage curiosity-driven research and welcome bold, untested concepts.

• Advance decentralized diffusion models (DDM) and pioneer next-generation architectures including rectified flows, EDM variants, and latent consistency models.
• Develop novel sampling algorithms, guidance mechanisms, and conditioning strategies that unlock new capabilities in controllable generation.
• Push the frontier of video generation and synthesis, including temporal modeling and multi-modal architectures.
• Publish at top-tier ML venues and share insights through blog posts, open-source contributions, and community engagement.

You are extremely curious. You actively consume the latest ML research - scanning arXiv, attending conferences, dissecting new open-source releases, and integrating breakthroughs into your own experimentation. You thrive on first-principles reasoning, see potential in unexplored ideas, and view learning as a perpetual process.

• Deep expertise in modern diffusion models including training, sampling, denoising schedulers, score matching, flow matching, consistency training, and distillation techniques.
• Experience with transformer architectures such as DiT, MM-DiT, and attention mechanisms.
• Hands-on experience with distributed training at scale across multi-GPU and multi-node setups, with familiarity in mixed-precision training (FP8, BF16).
• Experience with video generation and synthesis, including temporal modeling and 3D positional encodings.
• Knowledge of VAE architectures such as Hunyuan
  
  VAE, DC-AE, and latent representations, as well as motion modeling and optical flow.
• Strong mathematical foundation in SDEs, ODEs, optimal transport, and variational inference for designing novel generative objectives.

• Top of the market compensation and time to pursue open-ended research.
• Open source and publishing opportunities.
• In-person role at our Toronto office.
• Ownership of work that can set the direction for frontier diffusion models.
• Paid travel opportunities to the top ML conferences around the world.