We are Bagel Labs - an artificial intelligence research lab pioneering distributed training of frontier diffusion models on commodity hardware.
We ignore years of experience and pedigree. If you have high agency - meaning your default assumption is that you can control the outcome of whatever situation you are in - we want to hear from you. Every requirement below is flexible for a candidate with high enough agency and tolerance for ambiguity.
Role Overview
You will build the data foundation for our frontier video and image generation diffusion models, turning massive, messy collections of media into clean, well-labeled datasets that researchers can trust for training. You will own pipelines end to end and work closely with the modeling team to unblock experiments and catch data issues before they quietly degrade model quality.
Key Responsibilities
- Build pipelines that ingest, filter, and transform millions of video clips and images into training-ready shards.
- Run quality scoring and synthetic captioning at scale across GPU clusters.
- Own dataset versioning so researchers can trace any training run back to an exact snapshot.
- Optimize storage and compute to keep PB-scale processing fast and cost-efficient.
Who You Might Be
You stay on top of the latest vision model releases and are often one of the first to try new open-source tools when they drop. You have strong intuition for what makes a good training sample and get frustrated when bad data silently hurts model quality. You are pragmatic about making systems work reliably even when requirements shift mid-flight.
Desired Skills
- Comfort with video and image data at the file level, whether that means transcoding, cropping, or detecting scene boundaries.
- Experience running filters, scorers, or captioning models across large media datasets.
- Python proficiency for batch processing and moving petabytes through object storage.
- Experience with large-scale storage systems including NAS, object storage, and distributed filesystems.
- Familiarity with text-conditioned generative models such as CLIP and T5 for embedding precomputation and captioning at scale.
- Basic understanding of video codecs and containers — knowing the difference between H.264/H.265, keyframe structures, and variable frame rates matters at this scale.
- Understanding of how diffusion models work is a plus.
- Active Hugging Face or GitHub presence with open-source contributions is a plus.
What We Offer
- Top of the market compensation with equity upside and time to pursue open-ended research.
- A deeply technical culture where bold, frontier ideas are debated, stress-tested, and built.
- In-person role at our Toronto office.
- Paid travel opportunities to the top ML conferences around the world.