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Black Forest Labs

Member of Technical Staff - Image / Video Generation

Black Forest Labs is hiring a Member of Technical Staff - Image / Video Generation. It pays €130k-€340k a year and Level rates it ; you can apply on Black Forest Labs.

Level's read on this job

  • Builds AI ●●●● is how Level rates this role: the job is building AI systems.
  • The posting mentions PyTorch, Stable Diffusion and fine tuning among its tools and skills.
  • Level classes it as a lead role.
  • Black Forest Labs has 14 other open roles on Level.

AI in this role

PyTorchStable Diffusion
fine-tuning

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.

  • 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.

How we rate this

Member of Technical Staff - Image / Video Generation at Black Forest Labs rates 91 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Fine-tuningPyTorchStable Diffusion

Questions you could be asked

  1. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  2. Walk me through how you've used PyTorch in your day-to-day work.
  3. What are the limits of Stable Diffusion that you've run into, and how did you work around them?
  4. How would you decide a model or AI system is ready to ship?
  5. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: Fine-tuning, PyTorch, and Stable Diffusion. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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