About the Role
As a Human Data AI Researcher at Labelbox Labs, you will be at the forefront of developing cutting-edge methods to create, analyze, and leverage high-quality human-generated data for frontier model developers. Your role will involve designing and implementing advanced systems that incorporate human feedback into AI training processes, such as Reinforcement Learning from Human Feedback (RLHF). You will also work on innovative techniques to measure and improve human data quality, and develop AI-assisted tools to enhance the data labeling process. Your expertise in machine learning, human-AI interaction, and advanced human data integration techniques will be crucial in pushing the boundaries of AI capabilities and delivering state-of-the-art solutions to meet the evolving needs of our customers.
About You
- Ph.D. or Master's degree in Computer Science, Machine Learning, AI, Human-Computer Interaction, or a related field
- Strong background in machine learning, deep learning, and natural language processing
- Extensive experience with advanced human-AI interaction techniques, particularly RLHF and other human-in-the-loop learning methods
- Expertise in designing and implementing data quality measurement systems for human-generated data
- Deep understanding of frontier models (e.g., large language models, multimodal models) and their human data requirements
- Proficiency in programming languages such as Python, and experience with deep learning frameworks (e.g., PyTorch, TensorFlow)
- Familiarity with advanced AI alignment techniques and their practical applications
- Experience with human subject research, experimental design for data collection, and ethical considerations in AI
- Strong background in developing and implementing human preference learning algorithms
- Excellent research skills with a track record of publications in top-tier AI conferences or journals related to human-AI interaction
- Strong analytical and problem-solving abilities
- Excellent communication skills and ability to collaborate in a multidisciplinary team
Your Day to Day
- Conduct cutting-edge research on advanced methods for integrating human feedback into AI systems, including RLHF and other novel approaches
- Design and develop sophisticated systems to measure, enhance, and leverage the quality of human-generated data for AI training
- Create AI-assisted tools that incorporate active learning and adaptive sampling techniques to increase the efficiency and effectiveness of the human data labeling process
- Investigate the impact of different types of human feedback (e.g., demonstrations, preferences, critiques) on model performance and alignment
- Develop and implement novel algorithms for learning from human preferences and for optimizing the human feedback collection process
- Collaborate with engineering and product teams to integrate research findings into Labelbox's product suite, focusing on scalable and practical applications of human-AI interaction techniques
- Engage with customers and the AI community to understand evolving human data needs for frontier models and to share insights on best practices
- Publish research findings in top-tier academic journals and present at leading AI conferences
- Stay at the forefront of advancements in AI, particularly in areas related to human data quality, human-AI collaboration, and AI alignment
- Contribute to technical documentation, blog posts, and educational content to establish Labelbox as a thought leader in human-centric AI development
Research at Labelbox Labs
At Labelbox Labs, we're committed to pushing the boundaries of AI and data-centric machine learning, with a particular focus on advanced human-AI interaction techniques. We believe that high-quality human data and sophisticated human feedback integration methods are key to unlocking the next generation of AI capabilities. Our research team works at the intersection of machine learning, human-computer interaction, and AI ethics to develop innovative solutions that can be practically applied in real-world scenarios.
We foster an environment of intellectual curiosity, collaboration, and innovation. We encourage our researchers to explore new ideas, engage in open discussions, and contribute to the wider AI community through publications and conference presentations. Our goal is to be at the forefront of human-centric AI development, setting new standards for how AI systems learn from and interact with humans.
