ML Engineer Intern: Build Scalable AI Solutions

Engg; Palo Alto (CA); On-site; USD 34,440,000 - 48,216,000; Full time

Benefits offered by this job

Paid internship

On-site in Palo Alto

Lunch provided daily

Snacks and drinks

Job summary

Klaviyo in Palo Alto, CA offers a Summer Machine Learning Internship where you’ll work with ML and full-stack engineers to build models that extract insights from massive data streams and deliver customer value.

You’ll apply techniques from deep learning, NLP, and recommender systems to integrate AI into our product, learning from experienced engineers and contributing to high‑quality AI/ML platforms.

Qualifications

• Pursuing an advanced degree; PhD preferred in CS/AI, graduating Dec 2027 or May/June 2028.
• Previous internship experience in AI/ML or related fields with strong interest in building and deploying ML projects.
• Familiar with large-scale data processing techniques.
• Good communication skills and ability to collaborate with team members and stakeholders.
• Foundation in AI technologies: deep learning, recommender systems, NLP, generative AI, or related areas.
• Ability to code in Python and use ML tools like PyTorch.

Responsibilities

• Build high-impact ML systems with ownership of development, deployment and scale.
• Contribute to modeling, system design and implementation decisions.
• Contribute to development and execution of technical strategies aligned with business goals.
• Engage with internal stakeholders to understand needs and resolve blockers.
• Learn and improve engineering processes and agile development practices.
• Stay updated on AI trends and apply them within Klaviyo.

Skills

Python

PyTorch

Machine Learning

Deep Learning

NLP

Education

PhD preferred in Computer Science/AI

Tools

PyTorch

Spark

Job description

Klaviyo in Palo Alto, CA offers a Summer Machine Learning Internship where you’ll work with ML and full-stack engineers to build models that extract insights from massive data streams and deliver customer value.

You’ll apply techniques from deep learning, NLP, and recommender systems to integrate AI into our product, learning from experienced engineers and contributing to high‑quality AI/ML platforms.