Graduate Machine Learning Engineer Externship

RHAEOS

Mentors

Chase Correia, Director of Engineering, Rhaeos Inc.

About Us

Our research and development team builds advanced machine learning systems that translate complex physiological sensor data into meaningful clinical insights. We focus on engineering new sensing technologies to improve disease management, optimize clinical trials, and enhance patient care for individuals living with hydrocephalus.

Externship Description

Building robust AI tools for continuous remote monitoring requires massive amounts of data, but acquiring real-world clinical data often presents severe privacy and volume constraints. To solve this, the extern will architect and train generative machine learning models to synthesize high-fidelity, longitudinal sensor datasets that mimic continuous hydrocephalus monitoring. The extern will then utilize this synthetic data pipeline to train and evaluate downstream ML models designed to extract quantitative, actionable insights for disease management.

If you are interested time-series modeling, and building ML systems for healthcare, this externship will give you hands-on experience engineering pipelines that solve real-world data scarcity challenges and translating that to cutting edge medical technology development.

Specific Objectives

• Deliverable: An end-to-end machine learning pipeline for generating synthetic continuous sensor data, along with a downstream predictive model to extract quantitative clinical metrics.
• Domain Knowledge & Tasks: The extern will learn to architect generative models (e.g., GANs, VAEs, or diffusion models) for time-series data, build scalable ML pipelines, and evaluate model performance by comparing synthetic versus real-world data distributions.
• Training: The extern will receive regular one-on-one mentorship on machine learning engineering best practices, deep learning architectures, and the nuances of deploying AI for medical applications.

Qualifications

• Stage in Training: Currently enrolled Master’s, Doctoral, or Postdoctoral student.
• Disciplinary Background: Computer Science, Artificial Intelligence, Machine Learning Engineering, Software Engineering, or a related computational field.
• Technical Skills: Strong Python programming skills and practical experience with deep learning frameworks (such as PyTorch or TensorFlow). Familiarity with building ML pipelines, generative models, and handling complex time-series data is highly preferred.
• Professional Qualities: Strong systems-thinking, the ability to translate academic research into functional code, and a self-starter mentality capable of taking ownership of an ML project from architecture to prototype.