Data & AI Engineering Intern

【 About the company 】

At Tomofun, we LOVE pets and technology. Our vision is to bring joy and innovation to every pet lover in the world.

We are pioneers in the pet tech industry. Our debut product, Furbo - the world's first AIoT treat-tossing pet camera, is the #1 bestseller on Amazon in 15 countries, including the US, UK, and JP and have millions of users in 133 countries. In 2019, we launched an AI-powered subscription service, the Furbo Pet Nanny, which sends smart alerts to our users to notify them to check on the safety of their furbabies when they are home alone. Since the launch of Furbo Pet Nanny, we have saved hundreds of pets' lives and are continuing to innovate and develop new features with our state-of-the-art technology.

The learning curve is steep. But the work is exhilarating. Tomofun is on the cusp of a pet-tech boom and we’re looking for smart, innovative thinkers who aspire to incredible things.

 

> Know more about us: furbo.com | LinkedIn | Medium Tech Blog

【 About the role 】
Our centralized Data Team is building an Agentic Data Ecosystem: a unified semantic layer, a User 360 foundation, and AI agents that help teams across the company get answers from data without waiting on an analyst.
As a Data & AI Engineering Intern, you'll work directly with our data engineers, analytics engineers, and data scientists on three hands-on areas. You'll use AI agents to automate data collection, keep our ingestion pipelines healthy, and help make our internal Analytics Agents more accurate. This isn't a "make coffee and watch" internship. What you build will be used by marketing, growth, and product teams across our global offices.
【 What You'll Do 】

• AI-powered data collection: Use AI tools and agents (e.g. Claude, browser automation, scheduled workflows) to set up recurring collection of marketing data, including from advertising and e-commerce platforms that don't offer a public API.
• Data ingestion & cleaning pipelines: Maintain and monitor ingestion and cleaning pipelines in Databricks. Investigate failed jobs, fix schema changes caused by upstream platform updates, and help catch data discrepancies before stakeholders do.
• Agent answer quality: Review and tag question–answer pairs from our internal Analytics Agents, build evaluation sets, and help fine-tune prompts, semantic definitions, and knowledge sources to improve answer accuracy.