Junior Machine Learning Engineer

As a Junior Machine Learning Engineer, you’ll support the design, development, and deployment of ML models that drive real-world outcomes, while learning from experienced engineers and data scientists who are shaping how AI transforms the public sector. 

• Support the end-to-end lifecycle of machine learning solutions—from data collection and preprocessing to model training, testing, and deployment. 
• Work with large, complex, and often sensitive datasets to uncover insights and patterns that inform smarter decisions. 
• Apply your knowledge of Python and modern ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch) to build and refine predictive models. 
• Collaborate with senior engineers to optimize, scale, and automate ML workflows using AWS cloud services. 
• Participate in code reviews, peer learning sessions, and technical discussions to continually improve your skills. 
• Contribute to documentation, monitoring, and model evaluation to ensure accuracy, transparency, and compliance with healthcare and federal standards. 

Qualifications

• 1–3 years of hands-on experience in machine learning, data science, or software engineering (academic, internship, or professional). 
• Bachelor's degree in computer science, Data Science, Mathematics, or related technical field. 
• Strong foundation in Python programming and familiarity with ML frameworks and data manipulation libraries (NumPy, pandas, etc.). 
• Understanding statistics, model evaluation, and feature engineering. 
• Familiarity with cloud environments (AWS preferred) and version control (Git). 
• Curiosity, initiative, and a growth mindset; you’re eager to learn, experiment, and iterate.