Sr. Applied Scientist

·       Design and implement end-to-end training pipelines to build and improve editing quality for both images and videos.

·       Lead core development for specific mid-training areas (e.g., image to image and image to video editing), while aligning with broader team strategy.

·       Identify quality gaps in generative models and propose targeted mid-training solutions.

·       Develop scalable workflows for data curation, data quality improvements, and distributed training.

·       Increase engineering velocity and reducing iteration cost by systematizing mid-training experimentation and deployment.

·       Partner closely with research, data, evaluation, infrastructure, pre-training and post-training teams to push the editing quality for both images and videos.

·       Mentor junior engineers and contribute to raising the technical bar of the mid-training team.

·       Master’s degree or Ph.D. in Computer Science, Machine Learning, or a related field preferred.

·       Proven track record in mid-training or continual pre-training of large-scale multimodal models, specifically on cross modality for image and video data.

·       Deep understanding of pre-training, mid-training and/or post training for multimodal generative models.

·       Deep understanding of modern diffusion-based architectures (DiT) and conditional generation and editing, e.g., instruct editing, to drive precise structural and temporal control in production environments.

·       Strong expertise in Vision-Language Models (VLMs), including experience with contrastive learning, multimodal alignment, and leveraging VLM-based encoders to improve semantic understanding in generative tasks.

·       Ability to design and implement scalable pipelines for data curation, data quality control, and distributed training in collaboration with data and infrastructure teams.

·       Experience optimizing model inference and deployment for high-throughput product environments, ensuring a balance between generative quality and computational efficiency.

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Colorado:

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