Black Forest Labs

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.

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.

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.

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.

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.

Designs, deploys, and maintains ML infrastructure for training and inference clusters, optimizing for researcher iteration speed and production inference performance. Focuses on cloud platforms, Kubernetes, Slurm, IaC, and CI/CD for ML workflows.

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.