Research Scientist, Foundation Model (Video Generation)

Role overview

As a key member of our research team, you will design and implement core technologies, develop new methodologies for large-scale multimodal pre-training/mid-training (text, image, audio, and video), and drive innovative approaches for foundational model architecture. You will collaborate closely with engineering and product teams, shaping the future of real-time creative and agentic platforms at scale. What You’ll Do Lead research and development on pre-training and mid-training of multimodal foundation models at scale. Design and prototype novel algorithms and architectures for high-fidelity, real-time multimodal synthesis and interaction across modalities.

What you'll do

• Lead research and development on pre-training and mid-training of multimodal foundation models at scale.
• Design and prototype novel algorithms and architectures for high-fidelity, real-time multimodal synthesis and interaction across modalities.
• Focus on scalable data pipeline curation and model training strategies for broad, diverse, and sensory-rich datasets.
• Advance state-of-the-art techniques in diffusion, autoregressive, and other generative models for large-scale pre-training and fine-tuning.
• Identify, create, and leverage large, high-quality cross-modal datasets.
• Bring research advancements into production-ready systems in collaboration with engineering and product teams.
• Publish work in top-tier conferences and journals, and clearly communicate research both internally and externally.
• Stay at the forefront of foundational model and real-time multimodal AI research.

What the employer is looking for

• 5+ years of research experience in large-scale pre-training/mid-training of multimodal foundation models (LLMs, VLMs, Audio LMs, or similar), ideally at the staff or lead scientist level.
• Track record as a first author on major publications in top conferences or journals (e.g., NeurIPS, ICML, ICLR).
• Extensive hands-on experience with large-scale multimodal model design, training, and deployment.
• Deep understanding and implementation experience with generative architectures (diffusion, autoregressive, cross-modal, etc.).
• Expertise in high-throughput, scalable dataset curation and model pipeline optimization for multimodal applications.
• Strong programming and prototyping skills (Python, PyTorch, TensorFlow, etc.) and experience deploying research into production systems.
• Excellent communication and collaboration skills, and a passion for building creative enabling technology.