Research (Professional) Escobar and Hu Lab
About the role
The Research Assistant will play a core technical role in the PROXIMO project, an interdisciplinary clinical AI research initiative focused on privacy‑preserving data preparation, responsible development, and evaluation of large language model (LLM)–based mental health support chatbots. The assistant will lead and optimize technical workflows involving de‑identified conversational data, driving the development of robust data pipelines, quality‑control protocols, and scalable research architectures. This position is highly suited for a Master’s or Ph.D. student with a strong background in artificial intelligence, natural language processing (NLP), and large language models, looking to apply advanced technical skills to complex healthcare technology and data privacy challenges.
Responsibilities
- Design, optimize, and maintain advanced Python pipelines for conversational text processing.
- Implement and refine privacy‑preserving workflows using state‑of‑the‑art anonymization tools (e.g., Microsoft Presidio).
- Construct structured, high‑quality datasets for LLM fine‑tuning and evaluation.
- Establish rigorous, reproducible research frameworks for the PROXIMO team.
- Drive the development and quantitative/qualitative evaluation of LLM‑based mental health support chatbots.
- Architect multi‑turn conversational data for model training/evaluation.
- Design sophisticated prompt engineering and evaluation metrics.
- Evaluate model outputs for safety, empathy, tone, and clinical escalation protocols.
- Translate complex technical findings into actionable insights for an interdisciplinary research team.
- Audit, refactor, and scale existing code and documentation, taking ownership of the technical infrastructure.
- Collaborate closely with domain experts in psychiatry, psychology, computer science, and engineering.
- Main