About Black Forest Labs

We’re the team building the foundational layer for visual intelligence: multimodal models trained jointly on images, video, audio, and action to see, generate, and understand the world.

Our founding team pioneered latent diffusion and Stable Diffusion, the breakthroughs that made generative AI accessible to millions. With FLUX, we're pushing into generative media at scale, machines that act in the physical world, and emerging applications in computer use.

Our FLUX models top public benchmarks and run in production in some of the world's biggest platforms. We're a small, fast growing research lab competing with the largest companies in the world. From our labs in Freiburg to San Francisco, we're building what comes next.

Why This Role

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.

What You’ll Work On

Trains large-scale diffusion transformer models for image and video data, working at the scale where intuitions break and empirical evidence matters

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

Reasons about the speed-quality tradeoffs of neural network architectures in production settings where both constraints matter simultaneously

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

What We’re Looking For

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.

You likely have

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

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

Deep understanding of how to effectively evaluate image and video generative models—knowing which metrics correlate with quality and which are just convenient proxies

Strong PyTorch fluency; you write research code that others can build on. You work fluently with coding agents to move faster, and have the depth to verify their output and own the result.

Experience running multi-agent workflows: you know which tasks to hand off and which to keep yourself, and you use the speed-up to raise the quality bar, not lower it.

Solid understanding of distributed training techniques—FSDP, low precision training, model parallelism—because our models don't fit on one GPU and training decisions impact research outcomes

We'd be especially excited if you

Have experience writing forward and backward Triton kernels and ensuring their correctness while considering floating point errors

Bring proficiency with profiling, debugging, and optimizing single and multi-GPU operations using tools like Nsight or stack trace viewers

Know the performance characteristics of different architectural choices at scale

Have published research that contributed to how people think about generative models

How We Work Together

We’re a distributed team with real offices that people actually use. This role is based in Freiburg, and you’ll join us in the office at least 2 days a week, or one full week every other week.

Everything we do is grounded in four values

Obsessed. We are a frontier research lab. The science has to be right, the understanding deep, the product beautiful.