Krea AI
Multimodal · Real-time AI image generation
Currently tracking 3 active AI roles, down 12% versus the prior 4 weeks. Primary focus: Ship · Engineering.
Multimodal · Real-time AI image generation
Krea AI currently has 3 active AI-related roles in our index. The most common open titles are: Engineer, Supercomputing & Distributed Systems, ML Engineer - Personalization & Recommendation Systems, Machine Learning Engineer. Most positions are in Engineering.
Krea AI's active AI hiring is concentrated in: application (33%), pre-training (33%), data (33%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Krea AI is hiring AI talent in: United States (3 roles).
Job postings at Krea AI most frequently reference: model serving, fine tuning, vision, inference infra, recommender systems.
| Title | Stage | AI score |
|---|---|---|
| Machine Learning Engineer Machine Learning Engineer at Krea AI, focusing on large-scale training of foundation diffusion models for image and video generation, including controllability modules. The role involves developing novel research techniques and implementing them in production, optimizing data pipelines, and conducting experiments on HPC clusters. | PretrainPost-train | 9 |
| ML Engineer - Personalization & Recommendation Systems ML Engineer to architect and build Krea's personalization and recommendation systems from scratch, focusing on user taste, content curation, and adapting generative models to individual aesthetics. This role involves designing algorithms, building curated feeds, and contributing to personalized image generation research, taking systems from research to production. | ShipAgent | 8 |
| Engineer, Supercomputing & Distributed Systems The role focuses on building and operating the infrastructure for AI research and inference, including distributed training, large GPU clusters, petabyte-scale data pipelines, custom distributed datastores, job orchestration systems, and streaming pipelines. It involves designing multi-stage data pipelines, managing distributed training and inference on large GPU clusters, scaling workloads, and optimizing dataloaders and networking for large training runs. The role requires strong systems thinking and experience with distributed systems, Python, Kubernetes, and data tools. | Data | 7 |