About the job
Genmo is a pioneering research laboratory dedicated to advancing cutting-edge models for video generation, with the mission of unlocking the creative potential of Artificial General Intelligence (AGI). We invite you to be a part of our innovative team, where you can contribute to shaping the future of AI and expanding the horizons of video generation technology.
Role Overview:
We are on the lookout for a talented Research Scientist to join our dynamic team, specializing in alignment and post-training methodologies for large-scale video generation models. In this pivotal role, you will be instrumental in ensuring our diffusion-based video models consistently deliver high-quality, physically accurate, and safe outputs that align with human values and preferences.
Key Responsibilities:
Lead groundbreaking research initiatives in alignment and post-training strategies for video generation models, prioritizing enhanced quality, reliability, and alignment with human intent.
Design and implement supervised fine-tuning and reinforcement learning from human feedback (RLHF) pipelines for video generation models.
Establish robust evaluation frameworks to assess model alignment, safety, and output quality.
Create and optimize data collection pipelines for capturing human feedback and preferences.
Conduct experiments to validate alignment techniques and their scalability.
Collaborate with cross-functional teams to incorporate alignment enhancements into our production workflow.
Stay abreast of the latest developments by reviewing academic literature in generative AI and alignment.
Mentor junior researchers and promote a culture of responsible AI development.
Partner closely with product teams to ensure that alignment methods enhance model capabilities.
Qualifications:
Ph. D. in Computer Science, Artificial Intelligence, Machine Learning, or a closely related field.
Demonstrated excellence with a strong publication record in top-tier conferences (e.g., NeurIPS, ICML, ICLR) focusing on reinforcement learning, alignment, or generative models.
Extensive experience in implementing and optimizing large-scale training pipelines utilizing PyTorch.
In-depth understanding of reinforcement learning techniques, especially RLHF.
Proficient in distributed training systems and conducting large-scale experiments.
Proven ability to design and implement robust evaluation strategies for models.

