AI/ML Intern Summer 2027

Veeam Software•San Jose, CA

14h•$40 - $50•Onsite

About The Position

Veeam is the Data and AI Trust Company, specializing in helping organizations ensure their data and AI are fully understood, secured, and resilient to enable the acceleration of safe AI at scale. As the market leader in both data resilience and data security posture management, Veeam is built for the convergence of identity, data, security, and AI risk. Headquartered in Seattle with offices in more than 30 countries, Veeam protects over 550,000 customers worldwide, who trust Veeam to keep their businesses running. Join us as we go fearlessly forward together, growing, learning, and making a real impact for some of the world’s biggest brands. 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 experienced designed to support your personal and professional growth. The program takes place from June – August 2027 (10-week program).

Requirements

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
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
This role requires you to be in office 5 days a week at the San Jose, California location

Nice To Haves

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.

Responsibilities

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.