Black Forest Labs
Multimodal · Flux image-generation foundation models (Berlin)
Black Forest Labs currently has 8 active AI-related job listings. The listings are evenly distributed across post-training, pre-training, and data stages, each representing 25% of the open roles. Engineering is the most frequent function, with 5 roles, while Research has 3. The company is hiring for roles that frequently mention multimodal, fine-tuning, and model serving technologies. Over the last 30 days, Black Forest Labs has seen a 100% decrease in new AI roles, with 0 new listings compared to 2 in the previous 30-day period.
Currently tracking 8 active AI roles, down 29% versus the prior 4 weeks. Primary focus: Serve · Engineering. Salary range $180k–$300k (avg $240k).
Multimodal · Flux image-generation foundation models (Berlin)
Black Forest Labs currently has 8 active AI-related roles in our index. The most common open titles are: Forward Deployed, Robotics Engineer, Member of Technical Staff - Image / Video Generation, Member of Technical Staff - Infrastructure Engineer, Member of Technical Staff - Model Serving / API Backend Engineer, Member of Technical Staff - Post Training. Most positions are in Engineering and Research.
Black Forest Labs's active AI hiring is concentrated in: post-training (25%), pre-training (25%), data (25%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Black Forest Labs is hiring AI talent in: United States (1 role).
Job postings at Black Forest Labs most frequently mention: Generative AI, Image Generation, Video Generation, Scalability, PyTorch.
In the past 30 days, Black Forest Labs has posted 0 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Member of Technical Staff - Model Serving / API Backend Engineer This role focuses on bridging the gap between AI research breakthroughs and production systems by building and optimizing inference services and high-performance APIs for generative models. The engineer will own the productionization of research checkpoints, ensuring low latency, high throughput, and scalability for millions of requests, while also improving monitoring and observability. | Serve | 8 |