Open Postdoctoral position, faculty mentor Philip Fisher

Faculty Sponsor: Philip Fisher, Stanford Graduate School of Education. Appointment term: one year, starting Fall 2026.

About the Role

The Stanford Center on Early Childhood (SCEC) and the Stanford Institute for Human-Centered AI seek a postdoctoral scholar to join an AI-enabled instructional coaching initiative. This work advances a broader agenda of AI for high-stakes social interactions, addressing a key challenge in early childhood and beyond: improving human interactions in education, healthcare, and social services that are difficult to observe, measure, and scale. The role focuses on developing AI methods that learn from real expert practice while adhering to stringent privacy, consent, and data-governance requirements.

The SCEC has developed iFIND, an interactive AI-assisted video editing platform that uses transcript-based models to help instructional coaches identify salient classroom moments and generate targeted feedback. The postdoc will work with a benchmark dataset of social interactions involving young children (developed with Serena Yeung-Levy’s MARVL lab) and a dataset of videos from ongoing SCEC programs to extend iFIND’s current text-based approach with multimodal models.

Responsibilities

• Human-in-the-loop model adaptation: Design methods to convert coach interactions (e.g., selections, edits, rejections of AI-generated suggestions) into usable supervision for model adaptation. Determine which transcript, audio, and video data can be incorporated into training while complying with consent, privacy, and regulatory requirements. Develop approaches for learning from sparse, noisy feedback, including multimodal signal integration, temporal identification of salient moments, and model adaptation from implicit human feedback. Build infrastructure to monitor model performance, calibration, and variation across deployment contexts.
• User study: Lead the design and execution of a study evaluating the modeling approach in real early childhood intervention settings. This may include usability and time studies, comparative evaluations of coaching feedback efficiency/fidelity with vs. without AI assistance, and interviews with coaches about their tool experience.
• Mentorship and collaboration: Work under the mentorship of Dr. Philip Fisher and collaborate closely with SCEC Senior Machine Learning Engineer Dr. Lauren Klein Dubin. Publish research with SCEC faculty/staff and present findings to academic, practitioner, and policy audiences. Engage with the broader Stanford HAI research community.

Qualifications

• PhD with extensive experience in modern deep neural network-based techniques.
• Strong record of research or applied work in deep learning, including designing, training, and deploying large-scale models.
• Expertise in at least one of: computer vision, speech recognition, or multimodal learning, with experience in real-world technology deployment.
• Experience with human-in-the-loop machine learning or a strong foundation in related areas (e.g., human feedback signals, weak/noisy supervision, interactive ML systems).

Preferred Qualifications

• Experience deploying models to cloud environments (e.g., AWS).
• Experience with or strong interest in privacy-preserving ML or human subjects research.
• Passion for child health and development.

Benefits

• $90,000 annual salary.
• $2,000 research/conference allowance.
• Relocation support: $3,000 (international), $2,000 (cross-country), or $1,000 (West Coast, outside Bay Area).