Black Forest Labs

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.

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.

Infrastructure engineer focused on building and optimizing data systems for large-scale AI model training runs at peta-to-exabyte scale, involving scalable data loaders, efficient storage, and multi-cloud object storage abstraction.