Machine Learning Intern - AI

## About Our Summer Internship Program Our Summer Internship Program is designed for students entering their final year of university who are eager to gain meaningful, real-world experience in a fast paced, collaborative, and professional environment. As a Summer Intern, you'll participate in a comprehensive onboarding experience led by our University Relations team to set you up for success from day one. Throughout the program, you'll also have the opportunity to participate in weekly professional development sessions, networking events, social activities, and other engaging experiences designed to support your personal and professional growth. The program takes place from June – August 2027 (10-week program). ## What We're Looking For - Passion & Curiosity: Strong interest in machine learning research, experimentation, and understanding model behavior. - Machine Learning Fundamentals: Strong foundation in supervised/unsupervised learning, optimization, regularization, model evaluation, and deep learning fundamentals. - Model Training Experience: Hands-on experience training deep learning models in PyTorch or TensorFlow. Ability to diagnose poor convergence, overfitting, unstable training, gradient issues, data leakage, and weak generalization. - Statistics & Experimentation: Strong understanding of probability, statistics, hypothesis testing, experimental analysis, and interpreting noisy results. - Software Engineering Discipline: Ability to write clean, maintainable code with strong encapsulation, separation of concerns, modularity, and object-oriented design principles. - Rapid Prototyping: Comfortable using Claude or similar AI tools for development, debugging, and rapid iteration. - Research Mindset: Ability to independently investigate problems, design experiments, and analyze outcomes critically. ## Nice To Have - LLM Experience: Experience training, fine-tuning, or evaluating transformer models or LLMs. - Modern ML Tooling: Familiarity with Weights & Biases, MLflow, distributed training, mixed precision, LoRA/QLoRA, or hyperparameter optimization. - Research Exposure: Experience reproducing papers, participating in ML competitions, contributing to research projects, or building advanced personal projects. - Applied AI Domains: Exposure to NLP, generative AI, multimodal systems, retrieval systems, or recommendation systems. ## What You Could Be Working On - Model Training & Evaluation: Train and improve ML models across a variety of datasets and tasks. - Training Diagnostics: Analyze loss curves, gradients, metrics, and experiments to diagnose model failures and improve performance. - LLM & Generative AI Research: Work on transformer models, fine-tuning workflows, evaluation systems, and generative AI applications. - Rapid Experimentation: Prototype and iterate quickly using Claude-assisted development workflows. - Research Tooling: Build reusable experimentation, training, and evaluation workflows for ML research.Candidates should have completed advanced coursework in areas such as Machine Learning, Deep Learning, Probability & Statistics, Linear Algebra, Optimization, Algorithms & Data Structures, Artificial Intelligence, Natural Language Processing, Computer Vision, Reinforcement Learning, and Software Engineering. ## Targeted Field of Study Currently pursuing a Master’s degree or PhD in: Computer Science, Artificial Intelligence, Machine Learning, Statistics, Applied Mathematics, Data Science, Electrical Engineering, or other closely related quantitative fields.