ML Engineer Intern: Build AI-Driven Systems

Klaviyo Campus; Palo Alto (CA); On-site; USD 55,000 - 75,000; Full time

Benefits offered by this job

Lunch provided

Free snacks and drinks

Onsite internship in Palo Alto

Job summary

Klaviyo invites applications for a Summer Machine Learning Engineer Internship in Palo Alto, CA. You’ll join a team of ML and Full-stack engineers to build models extracting insights from large data streams and apply deep learning, NLP, and recommender system techniques to enhance our product.

You will contribute to model development, deploy scalable solutions, and learn from senior engineers while working on high-impact projects in a collaborative, campus-like environment.

Qualifications

• Foundational knowledge in ML or data science, deep learning, recommender systems, NLP, generative AI.
• Ability to code in Python and use ML frameworks like PyTorch.
• Internship experience in AI/ML or related fields preferred.
• Strong communication and collaboration with stakeholders.

Responsibilities

• Build ML systems with ownership over model development, deployment, and scale.
• Contribute to modeling, system design and implementation decisions.
• Develop technical strategies aligned with business goals and AI/ML platform reliability.
• Collaborate with internal stakeholders to understand needs and unblock work.
• Improve engineering processes including recruiting, performance development, and agile practices.
• Stay updated on AI trends and apply them to Klaviyo’s ecosystem.
• Plan projects, define milestones, and identify dependencies to meet goals.

Skills

Python

PyTorch

Large-scale data

Education

PhD preferred in CS/AI

Tools

Spark

Job description

Klaviyo invites applications for a Summer Machine Learning Engineer Internship in Palo Alto, CA. You’ll join a team of ML and Full-stack engineers to build models extracting insights from large data streams and apply deep learning, NLP, and recommender system techniques to enhance our product.

You will contribute to model development, deploy scalable solutions, and learn from senior engineers while working on high-impact projects in a collaborative, campus-like environment.