AI/ML Intern (Summer 2027)

• 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 experienced designed to support your personal and professional growth
• The program takes place from June – August 2027 (10-week program)
• 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

Benefits

• Unlimited paid time off, plus 3 global VeeaMe Days for self-care
• Paid parental leave: 8 weeks for all parents, 16 weeks for birthing parents
• Medical, dental, and vision coverage from day one
• Mental health support, therapy sessions, and digital wellness tools via SupportLinc EAP
• 401(k) retirement plan with matching contributions up to annual limits
• Fertility, adoption, and surrogacy support through Maven, plus paid volunteer time
• AirVet: 24/7 virtual veterinary care at no cost
• Legal services, identity protection, and supplemental health insurance options
• Tax-advantaged spending accounts for healthcare, dependent care, and commuting
• Professional training and education, including courses and workshops, internal meetups, and unlimited access to our online learning platforms (LinkedIn Learning, Athena, O’Reilly) and mentoring through our MentorLab 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
• 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