About the Role
You will develop human-in-the-loop methods that use coach feedback as supervision for model adaptation. You will integrate transcript, audio, and video data while meeting consent, privacy, and regulatory requirements. You will build performance-monitoring infrastructure and lead a user study evaluating coaching efficiency and fidelity with and without AI support.
Requirements
- Hold a PhD
- Demonstrate extensive experience with modern deep neural network techniques
- Demonstrate the ability to design, train, and deploy large-scale deep-learning models
- Possess expertise in computer vision, speech recognition, or multimodal learning
- Have experience with real-world technology deployment
- Have experience with human-in-the-loop machine learning or a technical foundation in human feedback, weak or noisy supervision, or interactive machine learning systems
Responsibilities
- Design methods that convert coach interactions into supervision for model adaptation
- Determine how transcript, audio, and video data can be used consistently with consent, privacy, and regulatory requirements
- Develop methods for multimodal signal integration and learning from sparse, noisy feedback
- Build infrastructure to monitor model performance, calibration, and variation across contexts and populations
- Design and execute a user study of instructional coaching with and without AI support
- Evaluate coaching efficiency and fidelity and interview coaches about tool use
Benefits
- USD 2,000 research and conference allowance
- Relocation support up to USD 3,000 for international relocation, USD 2,000 for cross-country relocation, or USD 1,000 for west coast relocation outside the Bay Area
Hiring Process
Application review begins August 15, 2026.