Black Forest Labs
Multimodal · Flux image-generation foundation models (Berlin)
Currently tracking 8 active AI roles, up 17% 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 9 active AI-related job listings. The company is hiring across multiple stages of AI development, with serving infrastructure roles making up 33% of the openings, followed by post-training, pre-training, and data roles, each at 22%. Engineering is the most frequent function, with 6 positions, while research accounts for 3. The majority of these roles are located in the United States. Frequent technical tags include fine_tuning, model_serving, vision, and multimodal.
Black Forest Labs currently has 9 active AI-related roles in our index. The most common open titles are: Developer Experience Engineer, Forward Deployed Machine Learning Engineer, Member of Technical Staff - Image / Video Generation, Member of Technical Staff - Infrastructure Engineer, Member of Technical Staff - Large Scale Data Infrastructure. Most positions are in Engineering and Research.
Black Forest Labs's active AI hiring is concentrated in: serving infrastructure (22%), post-training (22%), pre-training (22%). 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 reference: model serving, fine tuning, vision, multimodal, frontier research.
In the past 30 days, Black Forest Labs has posted 1 new AI-related role.
| Title | Stage | AI score |
|---|---|---|
| Member of Technical Staff - Pretraining Research role focused on leading large-scale pretraining experiments for multimodal foundation models (image, video, audio), involving architecture, objective functions, and training algorithms. Requires prior experience leading pretraining for production models and strong distributed training skills. | Pretrain | 10 |
| Member of Technical Staff - VLM Research role focused on developing and integrating state-of-the-art vision-language models (VLMs) into the FLUX generative AI stack, innovating on architectures and improving multimodal understanding for enhanced generation quality and controllability. | PretrainPost-train | 9 |
| Member of Technical Staff - Post Training This role focuses on the post-training pipeline for multimodal generative models, including data strategy, reward modeling, preference optimization, distillation, and safety tuning. The goal is to improve model quality and align them with human intent, with a strong emphasis on shipping these improvements to users. | Post-train | 9 |
| Forward Deployed Machine Learning Engineer Machine Learning Engineer focused on deploying and optimizing generative AI models (specifically diffusion models) in production environments for customers. This involves deep product integrations, inference optimization, fine-tuning, and diagnosing performance bottlenecks. | ServePost-train | 9 |
| Member of Technical Staff - Image / Video Generation Research role focused on training and fine-tuning large-scale diffusion models for image and video generation, involving rigorous experimentation, ablation studies, and understanding speed-quality tradeoffs in production settings. | Post-train | 9 |
| Senior Solutions Architect Solutions Architect role focused on bridging the gap between generative AI research and customer production integrations. Responsibilities include customer onboarding, guiding on prompting, inference optimization, evaluation, and finetuning, creating technical enablement resources, and translating customer feedback to engineering and research teams. Requires deep understanding of generative AI, experience serving models in production, Python proficiency, and strong communication skills. | ServePost-train | 8 |
| Member of Technical Staff - Infrastructure Engineer Infrastructure Engineer role focused on building and maintaining the large-scale training platforms and research infrastructure that powers generative AI model development, including scaling compute clusters, ensuring reliability, and optimizing performance. | Data | 7 |