San Francisco, CA
Research Scientist (post-training)
What the posting asks for
- Names
- PyTorch
- Doctorate
- Named as required
Read out of the employer's own description. Absence means the posting does not say, not that the answer is no.
The role
We are Genmo, a research lab developing the world’s most sophisticated video world models to understand, simulate, and interact with the physical world. Our mission is to unlock the right brain of AGI. Join us in advancing physical intelligence and enabling robots to learn and act in a changing world.
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.
Key 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
Qualifications:
Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a closely related field
Must have:
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
Experience with distributed training systems and large-scale experiments
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
Ideal candidate will have:
Experience with diffusion models or other generative architectures
Background in fine-tuning large language models or generative models
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
Contributions to open-source projects in AI alignment or generative AI
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.
Genmo is an Equal Opportunity Employer. Candidates are evaluated without regard to age, race, color, religion, sex, disability, national origin, sexual orientation, veteran status, or any other characteristic protected by federal or state law. Genmo, Inc. is an E-Verify company and you may review the Notice of E-Verify Participation and the Right to Work posters in English and Spanish.
Published by Genmo on their own careers page and reproduced here unedited. Read it at Genmo.
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What this listing does not tell you
Listed 22 days. Of the 85 post-training and RL roles this board has watched from listing to removal, 11% were gone from their employer's careers page by day 22, and the median came down after 104 days. That is a description of other listings that have already ended, not a prediction about this one: this board records when a listing disappears, never why, and a posting still up is not on a clock it can see.
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