AI Intern, Veeam

• 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
• 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
• Software Engineering
• Targeted Field of Study
• Targeted Field of Study Currently pursuing a Bachelors or Master’s degree in:
• Computer Science
• Statistics
• Applied Mathematics
• Data Science
• Electrical Engineering
• Or other closely related quantitative fields

Desirable

• Our Summer Internship is designed for students leading into their final year of university, who want to gain real life work experience in a fast past, exciting and professional environment
• As a Summer Intern, you will participate in onboarding with the UR team that will set you up for success in your role. You will also benefit from professional development sessions, networking, and social events provided by the Internship Program
• The program takes place from June – August 2026 (12 week program)
• Language Model Development, Machine: Learned Threat Detection, Agent-Based: Interactive Training Simulators
• 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