Member of Technical Staff - Post Training

<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. Our models power the tools used by millions of creators, developers, and businesses worldwide, and FLUX is among the most advanced generative systems in the world.</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>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.</p> <p>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.</p> <h2><strong>What You'll Work On</strong></h2> <ul> <li>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</li> <li>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</li> <li>Work across modalities: text-to-image, image editing, multi-reference, and video post-training</li> <li>Build personalization and customization capabilities that let users adapt our models to their own creative style</li> <li>Design and maintain high-throughput fine-tuning and evaluation infrastructure to support rapid iteration across the research team</li> <li>Identify quality and alignment gaps through rigorous evaluation, then close them through targeted research and engineering</li> </ul> <p> </p> <h2><strong>What We're Looking For</strong></h2> <ul> <li>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</li> <li>Deep experience across the post-training stack, not just one slice: reward modeling, preference learning, RLHF/RLAIF, and personalization</li> <li>Com