About the Role: We're looking for a recent graduate or early-career engineer with a passion for machine learning to join our team. You'll work alongside experienced engineers and clinicians to improve our prediction analytics platform — including delivery time predictions, risk scoring models, and staffing optimization algorithms. This is a hands-on role with significant mentorship and training opportunities as you grow your ML engineering skills in a real-world healthcare environment. What You'll Work On: Assisting with ML model development and optimization for delivery time prediction; Learning to build and manage ML pipelines on AWS SageMaker; Processing and feature engineering on healthcare datasets (150K+ patient records); Supporting development of risk scoring models for maternal health indicators; Writing tests and documentation for ML systems; Collaborating with clinicians to understand clinical workflows and data; Learning MLOps best practices including model monitoring and deployment. Requirements: Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or related field; Coursework or project experience in machine learning or data science; Proficiency in Python and familiarity with ML libraries (scikit-learn, pandas, numpy); Understanding of fundamental ML concepts (supervised learning, model evaluation, feature engineering); Strong analytical and problem-solving skills; Eagerness to learn and grow in a fast-paced startup environment; Clear written and verbal communication skills. Nice to Have: Internship or project experience with ML/data science; Exposure to cloud platforms (AWS, GCP, or Azure); Familiarity with deep learning frameworks (PyTorch, TensorFlow); Interest in healthcare or clinical applications; Experience with SQL and relational databases. Tech Stack: Python, AWS SageMaker, scikit-learn, PyTorch, pandas, numpy, PostgreSQL, Docker, Jupyter, S3. How You'll Be Supported: Direct mentorship from senior engineers with ML and healthcare experience; Structured onboarding and training on our ML infrastructure; Regular code reviews with actionable feedback; Exposure to real production ML systems and clinical data; Clear path for growth and increasing technical ownership. Salary: $90,000 – $115,000 + equity.