Machine Learning Intern (R&D)

Job Description

June 2026 Machine Learning Intern (R&D) | Phamily

The Tone:
This is a Full-Time Internship at Phamily, located in New York, NY. Phamily, through Jaan Health, is an AI-based care management company that provides a core technology platform to transform chronic disease management for healthcare providers. This role is crucial for supporting the AI team in developing, testing, and optimizing machine learning models and AI-driven solutions. The intern will contribute to real-world projects that directly impact 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 Director of AI
• Mission: Advance cutting-edge AI in real-world healthcare environments to improve patient outcomes and operational efficiency.
• Tech Stack: PyTorch, TensorFlow, Hugging Face, Python

What You’ll Actually Do
• Design: Design and prototype novel ML approaches, especially in NLP, LLMs, and transformer architectures for healthcare use cases.
• Research: Conduct applied research through experimentation, evaluation, and model iteration.
• Develop: Develop prompting strategies, fine-tuning techniques, and retrieval workflows for AI systems.
• Build: Build evaluation frameworks that connect model performance to real-world healthcare outcomes.
• Deploy: Collaborate with engineering and product teams to deploy new AI-powered features.

The Must-Haves
• Background: MS or PhD candidate in Machine Learning, Computer Science, or a related field, with a strong background in deep learning, NLP, or LLMs.
• Experience: Hands-on experience with PyTorch, TensorFlow, or Hugging Face, along with proven ability to run experiments and derive insights from data.
• Skills: Solid Python skills, comfort working with real-world, messy datasets, and an interest in bridging research to production impact.
• Bonus: Experience with conversational AI, LLM evaluation, fine-tuning, or retrieval systems, or exposure to healthcare data or applied ML in regulated domains.