About the role
<h2><strong>About Black Forest Labs</strong></h2> <p>We’re the team behind Latent Diffusion, Stable Diffusion, and FLUX—foundational technologies that changed how the world creates images and video. We’re creating the generative models that power how people make images and video—tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we’re just getting started.</p> <p>Headquartered in Freiburg, Germany with a growing presence in San Francisco, we’re scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity.</p> <h2><strong>Why This Role</strong></h2> <p>You'll train large-scale diffusion models for image and video generation, exploring new approaches while maintaining the rigor that helps us distinguish meaningful progress from incremental tweaks. This isn't about following established recipes—it's about running the experiments that clarify which architectural choices matter and which are less impactful.</p> <h2><strong>What You’ll Work On</strong></h2> <ul> <li>Trains large-scale diffusion transformer models for image and video data, working at the scale where intuitions break and empirical evidence matters</li> <li>Rigorously ablates design choices—running experiments that isolate variables, control for confounds, and produce insights you can actually trust—then communicating those results to shape our research direction</li> <li>Reasons about the speed-quality tradeoffs of neural network architectures in production settings where both constraints matter simultaneously</li> <li>Fine-tunes diffusion models for specialized applications like image and video upscalers, inpainting/outpainting models, and other tasks where general-purpose models aren't enough</li> </ul> <h2><strong>What We’re Looking For</strong></h2> <p>You've trained large-scale diffusion models and developed strong intuitions about what matters. You know that at research scale, every design choice has tradeoffs, and the only way to know which ones are worth making is through careful ablation. You're comfortable debugging distributed training issues and presenting research findings to the team.</p> <p><strong>You likely have:</strong></p> <ul> <li>Hands-on experience training large-scale diffusion models for image and video data, with practical knowledge of common failure modes and what matters most in training</li> <li>Experience fine-tuning diffusion models for specialized applications—upscalers, inpainting, outpainting, or other tasks where understanding the domain matters as much as understanding the architecture</li> <li>Deep