Veeam Software
AIInternSummer2026
“AI Intern- Summer 2026 at Veeam Software. Skills: Machine Learning, Deep Learning, Statistics, Experimentation, Software Engineering, Rapid Prototyping, Research Mindset. 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.”
Industry & Context.
Ability to diagnose poor convergence, overfitting, unstable training, gradient issues, data leakage, and weak generalization.; Ability to independently investigate problems, design experiments, and analyze outcomes critically.
This role requires you to be in office 5 days a week at the San Jose, California location, Applicants must be authorized to work in the U. S., We are unable to sponsor or take over sponsorship of an employment Visa now or in the future.
What They're Looking For.
Must Have
Machine Learning Fundamentals: 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: understanding of probability, statistics, hypothesis testing, experimental analysis, and interpreting noisy results., Software Engineering Discipline: Ability to write clean, maintainable code with 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., Currently pursuing a Bachelors or Master’s degree in: Computer Science Artificial Intelligence Machine Learning Statistics Applied Mathematics Data Science Electrical Engineering Or other closely related quantitative fields, Applicants must be authorized to work in the U. S., We are unable to sponsor or take over sponsorship of an employment Visa now or in the future.
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., 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
What You'll Do.
Model Training & Evaluation: Train and improve ML models across a variety of datasets and tasks.
Training Diagnostics: Analyze loss curves
and experiments to diagnose model failures and improve performance.
LLM & Generative AI Research: Work on transformer models
fine-tuning workflows
and generative AI applications.
Rapid Experimentation: Prototype and iterate quickly using Claude-assisted development workflows.
Research Tooling: Build reusable experimentation
and evaluation workflows for ML research.
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