About the role
Posted
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
Post-training is where a foundation model becomes a product. In this role, you'll own the post-training pipeline for our multimodal models end to end — from data strategy and reward modeling to preference optimization, distillation, and safety tuning — across image, editing, and video. You'll drive measurable gains in model quality, build the infrastructure that lets the whole research team iterate fast, and push the state of the art in what it means to align a generative model to human intent.
This is a Staff / Senior IC role. We're looking for someone who has shipped post-training for a frontier model before and wants to do it again.
What You'll Work On
Own the full post-training pipeline end to end — from data curation and reward modeling through fine-tuning, preference optimization, distillation, safety tuning, evaluation, and deployment
Advance techniques across the post-training stack: SFT, RLHF, RLAIF, DPO, preference learning, and reward modeling to align models with human intent and aesthetic judgment
Work across modalities: text-to-image, image editing, multi-reference, and video post-training
Build personalization and customization capabilities that let users adapt our models to their own creative style
Design and maintain high-throughput fine-tuning and evaluation infrastructure to support rapid iteration across the research team
Identify quality and alignment gaps through rigorous evaluation, then close them through targeted research and engineering
What We're Looking For
You've owned post-training for a frontier generative model through release (SFT, preference optimization (DPO or RLHF), distillation, safety tuning) with measurable quality wins on human prefs or standard benchmarks
Deep experience across the post-training stack, not just one slice: reward modeling, preference learning, RLHF/RLAIF, and personalization
Comfortable working across modalities: text-to-image, image editing, multi-reference, and ideally video
Strong PyTorch fluency; you write research code that others can build on
Experience with distillation (LADD, DMD, consistency models, or similar) or with building high-throughput eval pipelines is a strong plus
Bias toward shipping: measurable model-quality improvements that reach users, not just papers
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.
Low Ego. The work speaks. The best idea wins, no matter who said it. Credit is shared. Nobody is above any task.
Bold. We take the ambitious bet. We ship, we do not wait for conditions to be perfect.
Kind. People over politics. We treat each other with genuine warmth. Agency without empathy creates chaos.
If this sounds like work you’d enjoy, we’d love to hear from you.
Base Annual Salary:
EU €130,000-€340,000 + Equity
Note: We may use AI-assisted tools to help organize applications. All hiring decisions are always made by our team.