ML Researcher - Image / Video Diffusion

Required

• Proven track record in working with image or video models at scale (publications or open-source contributions a plus).
• Strong proficiency in PyTorch and understanding of its inner workings.
• Strong background in distributed training paradigms such as FSDP, CP, SP, USP, TP, and EP. Knowing how different parallelism strategies work together and their tradeoffs.
• Experience in profiling and debugging large distributed training. Being comfortable with analyzing traces to identify bottlenecks and look for improvements.
• Good knowledge of low precision training / inference in FP8, NVFP4, and MXFP8.
• Solid understanding of diffusion model training pipeline across pretraining, midtraining, preference optimization, and reinforcement learning.
• Keeping up with the developments in related fields such as LLM, VLM, representation learning, and robotics research.
• Being comfortable working in a goal-oriented research environment.
• Having good judgement around when one should explore different training strategies and when it's time to commit to a specific strategy to scale compute and data.
• Comfortable working with underspecified goals. We expect every technical member to take an ambiguous research goal and break it down into concrete requirements, plans, experiment plan, and execution items.
• Good research taste — bias towards simplicity and methods that scale well with compute, data, and minimal human supervision.
• Ability to iterate rapidly, and propose creative research directions.