AI/ML Research Intern – Summer 2027 (San Jose)

Veeam Software; San Jose (CA); On-site; USD 55,000 - 69,000; Full time

Benefits offered by this job

Paid Holidays

Tech Stipend

Volunteer Time

Professional Development

Job summary

Veeam Software invites students to join its Summer Internship Program in San Jose, CA for 10 weeks (June–August 2027). You will be part of a fast-paced, professional team focusing on machine learning research and AI safety at scale.

As a Summer Intern, you’ll experience onboarding, weekly professional development, networking events, and hands-on model training using PyTorch or TensorFlow, with Claude tooling support, and a strong emphasis on experimentation and results.

Qualifications

• Enrollment in Master’s or PhD program in CS/AI or closely related field.
• Strong ML fundamentals and coursework in DL/ML.
• Experience training DL models in PyTorch or TensorFlow; diagnose issues.
• Ability to work on-site in San Jose, CA five days a week.

Responsibilities

• Train and improve ML models across diverse datasets and tasks.
• Analyze training curves, gradients and metrics to diagnose failures.
• Contribute to LLM/generative AI research and evaluation workflows.
• Prototype rapidly using Claude-assisted development workflows.
• Build reusable experimentation, training, and evaluation tooling for ML research.

Skills

Passion & Curiosity

ML Fundamentals

Model Training

Statistics & Experimentation

Software Engineering

Rapid Prototyping

Research Mindset

Education

Master's or PhD in CS/AI/ML/Statistics/Data Science

Tools

Claude

Weights & Biases

MLflow

LoRA/QLoRA

Job description

Veeam Software invites students to join its Summer Internship Program in San Jose, CA for 10 weeks (June–August 2027). You will be part of a fast-paced, professional team focusing on machine learning research and AI safety at scale.

As a Summer Intern, you’ll experience onboarding, weekly professional development, networking events, and hands-on model training using PyTorch or TensorFlow, with Claude tooling support, and a strong emphasis on experimentation and results.