Staff Applied Scientist, Firefly Foundry

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Company Overview

A leading global technology organization is expanding its enterprise managed-service offering for custom multimedia Generative AI. The platform focuses on deeply tuned image, video, and 3D models built around customer intellectual property, combined with creative production workflows and media-intelligence capabilities. The solution is deployed across new and existing digital experiences and is gaining significant traction across Media and Entertainment, marketing, consumer retail, and other industries.

The organization is seeking a Staff Applied Scientist, Firefly Foundry to contribute directly to the development and advancement of large-scale generative AI models for image and video generation. The role will have a significant impact on model quality and next-generation creative workflows used by customers worldwide.

The successful candidate will join a team of applied researchers and engineers focused on advancing digital experiences. The role involves innovation across the full model training stack, collaboration across data, modeling, and product teams, and translating research into production-ready technologies.

The Opportunity

The Staff Applied Scientist will conduct research and development focused on large-scale, industry-level pre-training and mid-training of multimodal generative models. The position will contribute to improving image and video generation capabilities while supporting scalable creative workflows and enterprise applications.

The ideal candidate will have extensive practical experience training large-scale generative models, particularly for image and video generation and editing. Strong expertise in modern generative architectures, machine learning infrastructure, and research-to-production technology transfer will be highly valuable.

What You’ll Do

• Conduct innovative research and development in training large-scale generative AI models for image and video synthesis.
• Develop, implement, and rigorously evaluate advanced techniques for conditional generation and editing.
• Improve instruction compliance, controllability, and visual quality in pre-trained generative models.
• Collaborate with researchers, engineers, and product teams to translate multimodal AI innovations into scalable downstream applications.
• Develop and maintain robust evaluation pipelines for assessing generative models across quality, efficiency, robustness, and safety metrics.
• Translate research concepts and published work into production-ready implementations.
• Apply Python and modern machine learning frameworks to develop and optimize generative AI systems.
• Collaborate with product teams to support successful technology transfers from research into customer-facing applications.

Required Qualifications

• Ph.D. or M.S. in Computer Science, Machine Learning, or a related technical field.
• 8+ years of industry experience.
• Extensive practical experience in large-scale Generative AI training, particularly image and video generation and editing.
• Familiarity with diffusion models, transformers, or other state-of-the-art generative architectures.
• Strong coding and prototyping skills using Python and PyTorch.
• Experience working with machine learning infrastructure and large-scale model training systems.
• Excellent communication skills and the ability to collaborate effectively across cross-functional teams.
• Experience working with product teams on technology transfers.
• Strong publication record in Computer Science, Artificial Intelligence, Machine Learning, or a related field.

What You’ll Bring

• Experience with large-scale generative model training.
• Experience with synthetic data generation.
• Experience working with large-scale datasets.
• Experience collaborating with product teams on technology transfers.
• Strong research and development capabilities in multimodal Generative AI.
• Ability to translate research innovations into practical, production-ready solutions.
• Strong understanding of image and video generation technologies and modern machine learning architectures.

Disclaimer

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