AI/ML Intern (Summer 2027)

Join our Summer Internship Program as an AI/ML Intern, where you'll gain meaningful, real-world experience in a fast-paced, collaborative environment. Throughout the 10-week program, you'll participate in a comprehensive onboarding experience, weekly professional development sessions, networking events, and social activities. Your responsibilities will include model training and evaluation, training diagnostics, LLM and generative AI research, rapid experimentation, and research tooling. This internship is designed for students entering their final year of university who are eager to gain hands-on experience in machine learning and artificial intelligence.

• Participate in model training and evaluation, analyze diagnostics, and work on LLM and generative AI research.
• Prototype and iterate quickly using Claude-assisted development workflows, and build reusable experimentation, training, and evaluation workflows for ML research.
• Engage in professional development sessions, networking events, and social activities to support personal and professional growth.

• 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
• Machine Learning Fundamentals: Strong foundation in supervised/unsupervised learning, optimization, regularization, model evaluation, and deep learning fundamentals
• 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
• Statistics & Experimentation: Strong understanding of probability, statistics, hypothesis testing, experimental analysis, and interpreting noisy results
• Passion & Curiosity: Strong interest in machine learning research, experimentation, and understanding model behavior
• Research Mindset: Ability to independently investigate problems, design experiments, and analyze outcomes critically
• 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
• Software Engineering
• Probability & Statistics
• Optimization
• Natural Language Processing
• Computer Vision
• Algorithms & Data Structures
• Machine Learning
• Artificial Intelligence
• Candidates should have completed advanced coursework in areas such as:
• Reinforcement Learning
• Deep Learning
• Linear Algebra
• 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
• Modern ML Tooling: Familiarity with Weights & Biases, MLflow, distributed training, mixed precision, LoRA/QLoRA, or hyperparameteroptimization
• LLM Experience: Experience training, fine-tuning, or evaluating transformer models or LLMs
• 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