Support the development, training, and evaluation of ML/AI models using Python and industry-standard frameworks
Apply supervised learning techniques such as Logistic Regression, Gradient Boosting Machines (GBM), and Random Forests to solve classification and regression problems
Explore and implement Generative AI models (e.g., transformers, diffusion models) for use cases such as text generation, summarization, or synthetic data creation
Analyze structured and unstructured datasets using pandas, NumPy, and matplotlib/seaborn for data wrangling and visualization
Use scikit-learn, XGBoost, and TensorFlow/PyTorch to build and validate predictive models
Contribute to software development projects using Python, Java, or Kotlin
Present findings, model performance, and project outcomes to teams
Participate in code reviews, team meetings, and brainstorming sessions to support collaborative innovation

Requirements

Currently enrolled in a Bachelor's or higher in Computer Science, Engineering, or a related field
Return to school in Fall 2026 to complete degree program
Experience with Python programming and familiarity with libraries such as pandas, scikit-learn, NumPy, and matplotlib
Exposure to machine learning concepts and algorithms, including classification, regression, and model evaluation
Familiarity with Generative AI concepts and frameworks (e.g., Hugging Face Transformers, OpenAI API, or similar)

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonLogistic RegressionGradient Boosting MachinesRandom ForestsGenerative AIpandasNumPymatplotlibscikit-learnTensorFlow

Soft Skills

collaborationpresentationcommunication