Research Scientist (post-training)

Responsibilities

• Lead research initiatives in alignment and post-training methods for video generation models, focusing on improved quality, reliability, and adherence to human intent
• Design and implement supervised fine-tuning and reinforcement learning from human feedback (RLHF) pipelines for video generation models
• Develop robust evaluation frameworks to measure model alignment, safety, and output quality
• Create and optimize data collection pipelines for human feedback and preferences
• Design and conduct experiments to validate alignment techniques and their scaling properties
• Collaborate with cross-functional teams to integrate alignment improvements into our production pipeline
• Stay at the cutting edge of the field by regularly reviewing academic literature in both generative AI and alignment
• Mentor junior researchers and foster a culture of responsible AI development
• Work closely with product teams to ensure alignment methods enhance rather than inhibit model capabilities

Requirements

• Strong publication record in top-tier conferences (e.g., NeurIPS, ICML, ICLR) with a focus on reinforcement learning, alignment, or generative models
• Extensive experience implementing and optimizing large-scale training pipelines using PyTorch
• Deep understanding of reinforcement learning techniques, particularly RLHF
• Proven track record in designing and implementing robust evaluation frameworks
• Excellent communication skills with the ability to explain complex technical concepts to diverse audiences
• Strong software engineering skills and experience with complex shared codebases

Preferred

• Background in fine-tuning large language models or generative models
• Contributions to open-source projects in AI alignment or generative AI

Role overview

We are seeking an exceptional Research Scientist to join our team, focusing on alignment and post-training techniques for large-scale video generation models. In this role, you will be at the forefront of ensuring our diffusion-based video models reliably produce high-quality, physically accurate and safe outputs that match human preferences and values.

Qualifications

Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a closely related field

Must have

Experience with distributed training systems and large-scale experiments

Ideal candidate will have

Experience with diffusion models or other generative architectures

• Experience working with human feedback data collection and annotation pipelines
• Strong aesthetic sense and understanding of video quality assessment
• Familiarity with alignment techniques such as constitutional AI or debate

Track record of successful collaboration with product teams

Experience with perceptual quality metrics and human evaluation design

Additional Information

The role is based in the Bay Area (San Francisco). Candidates are expected to be located near the Bay Area or open to relocation.

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