Machine Learning Intern (R&D)

Job Description

Machine Learning Intern | Phamily

The Tone:
This is a full-time internship at Phamily, located in New York, NY, requiring three days per week onsite. Jaan Health, operating as Phamily, is a leading AI-based care management company that supports healthcare providers. It utilizes proprietary technology to enable health systems, medical groups, and ACOs to deliver high-quality, proactive care to a large patient base. The company’s core platform, Phamily, transforms chronic disease management with clinically tested AI and user-friendly technology, reducing labor investment and overall care costs. This role is critical as it supports the AI team in developing, testing, and optimizing machine learning models and AI-driven solutions, directly impacting patient outcomes and operational efficiency within healthcare.

The TL;DR
• Role: Internship
• Type: Full-Time Internship
• Location: Hybrid, New York, NY
• Pay: $30–$35 hourly
• Team: AI Team, reporting to the Director of AI
• Mission: Advance cutting-edge AI in real-world healthcare environments by building AI-powered infrastructure to transform healthcare from reactive treatment to proactive care, ultimately improving patient outcomes and reducing costs.
• Tech Stack: PyTorch, TensorFlow, Hugging Face, Python

What You’ll Actually Do
• Design: Design and prototype novel machine learning approaches, especially in Natural Language Processing (NLP), Large Language Models (LLMs), and transformer architectures for specific healthcare use cases.
• Research: Conduct applied research by driving experimentation, thorough evaluation, and continuous model iteration to advance AI-driven solutions.
• Develop: Develop effective prompting strategies, advanced fine-tuning techniques, and efficient retrieval workflows for optimizing language model performance.
• Translate: Translate validated research findings into robust, scalable, and production-oriented systems capable of transforming healthcare operations.
• Build: Build comprehensive evaluation frameworks that directly connect machine learning model performance with measurable healthcare outcomes and efficiency.

The Must-Haves
• Background: Currently an MS or PhD candidate in Machine Learning, Computer Science, or a closely related quantitative field.
• Experience: Possesses a strong foundation in deep learning, Natural Language Processing (NLP), and/or Large Language Models (LLMs), along with hands-on experience using frameworks like PyTorch, TensorFlow, or Hugging Face. Demonstrated ability to design and execute experiments and extract actionable insights from data.
• Skills: Solid Python programming skills, comfortable handling and working with real-world, often messy datasets, and an interest in transitioning research findings into production impact.
• Bonus: Experience with conversational AI, expertise in LLM evaluation, fine-tuning, or retrieval systems, or prior exposure to healthcare data or applied machine learning within regulated domains.