Forward Deployed Machine Learning Engineer

About Black Forest Labs 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. 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. Why This Role You'll live at the intersection of cutting-edge research and brutal production reality. Your customers won't just want FLUX to work—they'll need it optimized for their specific hardware, fine-tuned for their unique use cases, and integrated into systems that weren't designed for diffusion models in the first place. What You'll Work On Ensures FLUX models perform optimally in customer environments—whether that's on-premise GPU clusters or BFL-hosted infrastructure—balancing the eternal tension between latency and output quality Architects deep product integrations that go far beyond "here's an API endpoint"—helping customers with everything from model hosting and deployment to inference optimization techniques that haven't made it into textbooks yet Customizes our foundation models for visual media to solve problems customers couldn't articulate until you helped them understand what's possible Sits in technical deep-dives with customers to diagnose performance bottlenecks, then translates those findings into solutions (and sometimes into research questions for our core team) Discovers where generative visual AI should go next by understanding what industries are struggling with problems we could solve What We're Looking For You understand diffusion models not just conceptually, but viscerally—you've debugged them, optimized them, served them at scale. You've been in the room when a customer's integration goes wrong and you need to diagnose whether it's a model issue, an infrastructure issue, or a fundamental misunderstanding of what the model can do. You likely have: Direct experience working with customers on generative AI deployment—the kind where you're iterating on solutions in real-time, not just following a playbook Hands-on expertise with generative modeling approaches, particularly finetuning, optimizing, and serving deep learning models in production environments A proven track record as an ML engineer who's shipped models that real systems depend on Strong Python skills and intuitive understanding of API design (because demos and prototypes are how you communicate what's possible) The ability to explain why a diffusion model is slow to an executive and how to fix it to an engineer—in the same meeting We'd be especially excited if you: Have deep knowledge of diffusion models and/or flow matching, including finetuning and distillation techniques that go beyond standard tutorials Know the FLUX ecosystem intimately—ComfyUI, common training frameworks, the tools practitioners actually use Have battle-tested experience optimizing inference for transformer-based models (and the scars to prove it) Can architect solutions in complex enterprise environments where "just add more GPUs" isn't an option Contribute to open-source projects in the diffusion model space and understand the community Have deployed models on cloud platforms using state-of-the-art serving infrastructure How We Work Together We’re a distributed team with real offices that people actually use. Depending on your role, you’ll either join us in Freiburg or SF at least 2 days a week (or one full week every other week), or work remotely with a monthly in-person week to stay connected. We’ll cover reasonable travel costs to make this possible. We think in-person time matters, and we’ve structured things to make it accessible to all. We’ll discuss what this will look like for the role during o