Member of Technical Staff - Large Model Data

Member Of Technical Staff - Large Model Data

What if the bottleneck to better generative models isn't architecture or compute, but the quality and scale of the data we train on?

We're the ~50-person team behind Stable Diffusion, Stable Video Diffusion, and FLUX.1models with 400M+ downloads. But here's what we've learned : breakthrough models require breakthrough datasets. Not just big datasetscarefully curated, properly processed, deeply understood datasets that push models toward capabilities they couldn't achieve otherwise. That's the infrastructure you'll build.

What You'll Pioneer

You'll create the data systems that make frontier research possible. This isn't traditional data engineeringit's building infrastructure at a scale where billion-image datasets are normal, where video processing pipelines need to run across thousands of GPUs, and where understanding what's in your data is as important as collecting it.

You'll be the person who :

• Develops and maintains scalable infrastructure for acquiring massive-scale image and video datasetsthe kind where "large" means billions of assets, not millions
• Manages and coordinates data transfers from licensing partners, turning heterogeneous sources into training-ready pipelines
• Implements and deploys state-of-the-art ML models for data cleaning, processing, and preparationbecause at our scale, manual curation isn't an option
• Builds scalable tools to visualize, cluster, and deeply understand what's actually in our datasets (because you can't fix what you can't see)
• Optimizes and parallelizes data processing workflows to handle billion-scale datasets efficiently across both CPUs and GPUs
• Ensures data quality, diversity, and proper annotationincluding captioning systems that make training datasets actually useful
• Transforms user preference data and alternative sources into formats that models can learn from
• Works directly in the model development loop, updating datasets as training trajectories reveal what we're missing

Questions We're Wrestling With

Who Thrives Here

You understand that data engineering at research scale is fundamentally different from traditional data engineering. You've built pipelines that broke, debugged them at scale, and emerged with opinions about what works. You know the difference between data that looks good and data that actually trains well.

You likely have :

We'd be especially excited if you :

What We're Building Toward

We're not just processing datawe're building the foundation that determines what our models can learn. Every pipeline optimization makes training faster. Every data quality improvement makes models better. Every new data source opens new possibilities. If that sounds more compelling than maintaining existing systems, we should talk.

Base Annual Salary : $180,000$300,000 USD

We're based in Europe and value depth over noise, collaboration over hero culture, and honest technical conversations over hype. Our models have been downloaded hundreds of millions of times, but we're still a ~50-person team learning what's possible at the edge of generative AI.