Data Engineer for Generative Image and Video Models - Remote

Overview

Black Forest Labs is a cutting-edge startup pioneering generative image and video models. Our team, which invented Stable Diffusion, Stable Video Diffusion, and FLUX.1, is currently looking for a strong candidate to join us in developing large-scale data pipelines for training frontier models.

In Short

• Develop and maintain scalable infrastructure for large-scale image and video data acquisition
• Manage and coordinate data transfers from various licensing partners
• Implement and deploy state-of-the-art ML models for data cleaning, processing, and preparation
• Implement scalable and efficient tools to visualize, cluster, and deeply understand the data
• Optimize and parallelize data processing workflows to handle billion-scale datasets efficiently
• Ensure data quality, diversity, and proper annotation (including captioning) for training readiness
• Getting training data from alternative sources such as user preferences into trainable format
• Work closely in the model development loop to update data as necessitated by the training trajectory

Requirements

• Proficiency in Python and various file systems for data intensive manipulation and analysis
• Familiarity with cloud computing platforms (AWS, GCP, or Azure) and Slurm/HPC environments for distributed data processing
• Experience with image and video processing libraries (e.g., OpenCV, FFmpeg)
• Demonstrated ability to optimize and parallelize data processing workflows across CPUs and GPUs
• Familiarity with data annotation and captioning processes for ML training datasets
• Knowledge of machine learning techniques for data cleaning and preprocessing

Benefits

• Background or keen interest in developing large-scale data acquisition systems
• Experience with natural language processing for image/video captioning
• Experience with data deduplication techniques at scale
• Experience with big data processing frameworks (e.g., Apache Spark, Hadoop)
• Understanding of ethical considerations in data collection and usage