Summer Internship - AI Researcher at Aledade, Inc. in Remote

Summary: Aledade, Inc. is recruiting for Summer Interns in 2026 to engage in research and development projects within the healthcare sector. The internship focuses on designing and prototyping the Universal EHR Context Protocol, utilizing advanced AI techniques to enhance healthcare data extraction processes. Responsibilities: - Design a unified request/response schema that abstracts variations in proprietary EHR APIs, enabling downstream AI applications to request patient context agnostically - Develop the logic to detect incomplete or failed API requests and deploy a browser-use agent to locate and extract the missing context via the EHR's web interface - Build a reasoning layer utilizing LLMs/VLMs to process unstructured documents retrieved by the agent, extract required clinical elements, and map them to the UECP schema - Establish an evaluation framework to measure the operational tradeoffs between API retrieval and agentic fallback - Design caching strategies to mitigate latency, and implement automated LLM evaluation pipelines (e.g., LLM-as-a-judge) to assess extraction accuracy and clinical safety Required Qualifications: - Currently pursuing a Master's or PhD in Computer Science, Applied AI, Software Engineering, Health Systems Engineering, or a closely related discipline - Strong backend software engineering skills, primarily in Python, with a solid foundation in data structures, system architecture, and JSON schema design - Experience with web scraping, DOM manipulation, and browser automation frameworks (e.g., Playwright, Puppeteer, Selenium) - Practical experience integrating LLMs and Vision-Language Models (VLMs) for unstructured data extraction and reasoning Preferred Qualifications: - Proven experience or deep academic interest in building autonomous, browser-use agents, semantic routing, and fallback logic (e.g., LangChain, AutoGPT, or custom reasoning loops) - Understanding of standard healthcare data exchange protocols (like HL7 FHIR, SMART on FHIR), EHR API ecosystems, and clinical coding models like Hierarchical Condition Categories (HCC) - Ability to evaluate and optimize the operational tradeoffs of AI systems, specifically balancing latency, caching strategies, and extraction accuracy in real-time environments - Proficiency in using AI coding tools (e.g., Claude Code, Cursor) to quickly prototype and bypass boilerplate engineering tasks, keeping the focus on core routing architecture - High tolerance for ambiguity and the ability to independently research, test, and architect fault-tolerant systems in highly fragmented and unpredictable software ecosystems - Strong technical writing skills for potential academic publication Required Skills: Python, Backend software engineering, Data structures, System architecture, JSON schema design, Web scraping, DOM manipulation, Browser automation frameworks, Large Language Models (LLMs), Vision-Language Models (VLMs), Healthcare data exchange protocols, HL7 FHIR, SMART on FHIR, Hierarchical Condition Categories (HCC), Research autonomy