What if the gap between "our models are state-of-the-art" and "our customers are getting value" is someone who can speak both languages fluently?
Our founding team pioneered Latent Diffusion and Stable Diffusion - breakthroughs that made generative AI accessible to millions. Today, our FLUX models power creative tools, design workflows, and products across industries worldwide.
Our FLUX models are best-in-class not only for their capability, but for ease of use in developing production applications. We top public benchmarks and compete at the frontier - and in most instances we're winning.
If you're relentlessly curious and driven by high agency, we want to talk.
With a team of ~50, we move fast and punch above our weight. From our labs in Freiburg - a university town in the Black Forest - and San Francisco, we're building what comes next.
What You'll Pioneer
You'll be the bridge between our research frontier and customer reality. Not just explaining what our models do, but ensuring customers actually succeed with them—which means understanding their constraints, their use cases, and sometimes what they need even when they can't articulate it yet.
You'll be the person who:
- Onboards customers to our suite of models, providing hands-on guidance on prompting strategies, inference optimization, evaluation frameworks, and finetuning approaches that ensure best-in-class production integrations
- Works alongside our Sales and BD teams on the most complex and high-stakes customer projects—the ones where deployment success has material business impact
- Acts as BFL's central internal hub, seamlessly connecting go-to-market, engineering, and applied research teams so customer insights flow in both directions
- Creates reusable technical enablement resources that amplify our sales team's effectiveness and technical fluency—documentation, demos, integration guides that scale beyond individual conversations
- Translates customer technical feedback into actionable product insights, then collaborates with engineering and research teams to actually implement required updates and new features
Questions We're Wrestling With
- How do you teach customers to get the most out of generative models without overwhelming them with architectural details they don't need?
- When a customer deployment isn't working, how do you diagnose whether it's a model limitation, an integration issue, or a mismatch between expectations and capabilities?
- What does "production-ready" actually mean when every customer's constraints are different?
- How do you build technical resources that serve both engineers who want depth and executives who need clarity?
- Which customer pain points are one-off issues versus signals that we need to build new capabilities?
These aren't hypothetical—they're daily decisions that shape how our technology reaches the world.
Who Thrives Here
You understand generative AI deeply enough to debug customer integrations, but you're equally comfortable explaining business value to non-technical stakeholders. You've been in rooms where the sale depends on whether you can architect a solution on the spot. You get energized by translating between worlds—research to production, technical to business, problem to solution.
You likely have:
- Deep understanding of generative AI and hands-on experience serving generative deep learning models in production settings
- A track record of working directly with customers, iterating on solutions, and providing tailored support that actually moves the needle
- Proficiency in Python and intuitive understanding of API integrations—enough to implement basic functionality and help customers build prototypes and demos
- Experience explaining sophisticated technical concepts to both technical and business audiences without losing either group
- Excellent communication skills honed through collaborating with non-technical stakeholders, with the ability to adapt your message depending on who's in the room
We'd be especially excited if you:
- Have prior experience finetuning diffusion models and working with customization tools like ComfyUI
- Bring a proven track record in solutions engineering, particularly on large and complex enterprise deals
- Can architect solutions in complex enterprise environments where standard approaches don't work
- 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
What We're Building Toward
We're not just supporting customers—we're learning how frontier generative AI actually gets used in the real world. Every customer deployment teaches us something. Every technical challenge reveals product gaps we didn't know we had. Every successful integration becomes a template for the next. If that sounds more compelling than following a playbook, we should talk.
Base Annual Salary: $180,000–$300,000 USD
We're based in Europe and value depth over noise, collaboration over hero culture, and honest technical conversations over hype. Our models have been downloaded hundreds of millions of times, but we're still a ~50-person team learning what's possible at the edge of generative AI.
