About the RoleWe'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 deploymentRequirements- 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 skillsNice 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 databasesTech StackPython, AWS SageMaker, scikit-learn, PyTorch, pandas, numpy, PostgreSQL, Docker, Jupyter, S3Compensation$90,000 – $115,000 + equity