Quick overview
Job description
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. We’re creating the generative models that power how people make images and video—tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we’re just getting started.
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 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 proficiency in PyTorch, transformer architectures, and the full ecosystem of modern deep learning
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
Similar jobs
- OT

Machine Learning Engineer (Computer Vision & VLM) Auto Apply
Orcrist Technologies · Remote / Germany · Remote 🇩🇪
Background ChecksComputer VisionKubernetes+1TechnologySalary not listed1 week ago - OT

(Senior) Machine Learning Engineer Auto Apply
Orcrist Technologies · Remote / Germany · Remote 🇩🇪
MLOpsNLPConcrete+7TechnologySalary not listed1 week ago - BF

Applied Research Engineer, Evaluation Auto Apply
Black Forest Labs · Freiburg (Germany) · On-site 🇩🇪
LLMPyTorchPythonEngineeringSalary not listed1 week ago - BF

Member of Technical Staff - VLM Auto Apply
Black Forest Labs · Freiburg (Germany) · On-site 🇩🇪
LLMGenerative AISalary not listed6 months ago - BF

Member of Technical Staff - Post Training Auto Apply
Black Forest Labs · Freiburg (Germany) · On-site 🇩🇪
PyTorchSalary not listed6 months ago - BF

Member of Technical Staff - Pretraining Auto Apply
Black Forest Labs · Freiburg (Germany) · On-site 🇩🇪
LLMPyTorchPythonSalary not listed6 months ago - BA

Founding ML Researcher Auto Apply
Basecompute · Berlin · On-site 🇩🇪
CUDALLMSalary not listed2 months ago - CL

Robot Learning EngineerNew Auto Apply
Clera · Munich · On-site 🇩🇪
RoboticsComputer VisionDeep Learning+5EngineeringSalary not listed13 hours ago - ST
Senior Computer Vision Engineer (All Genders) Auto Apply
STARK · Germany · Hybrid 🇩🇪
RoboticsComputer VisionContinuous Improvement+4TechnologySalary not listed4 days ago - ST
Data & Machine Learning Engineer (All genders) Auto Apply
STARK · Munich · Hybrid 🇩🇪
GCPSQLAWS+12TechnologySalary not listed4 days ago - FO

Machine Learning Engineer (f/m/d) Auto Apply
Focused · Darmstadt · Hybrid 🇩🇪
FortranANSYSMLOps+7TechnologySalary not listed4 weeks ago - AR

Robot Learning Engineer (m/f/d) VLA & Foundation ModelsNew Auto Apply
Agile Robots SE · Munich · Hybrid 🇩🇪
RoboticsAssemblyPyTorch+3EngineeringSalary not listed2 days ago