Mission and Vision

We are passionate about recommender systems because they have a big impact on revenue and engagement across many industries — from e-commerce to media.

Our mission is to quickly answer the questions: How do you know that this recommender system is delivering good work? And, What’s the next area for improvement?

Our first goal is to turn the specialized knowledge behind evaluating and debugging recommender systems into a platform that sits on top of the customer’s data warehouse. We call this operational analytics for recommender systems.

Values

We value craftsmanship (taking pride in your work, proactively hunting for bugs), repeatability (automation and documentation), and maintaining a growth mindset (if we don’t understand something, we ask questions — that’s how we build great products!).

Funding

We’ve secured a $1.5M seed round from Bain Capital Ventures, Cadenza Ventures, Y Combinator, and angel investors like Brad Klingenberg (former chief algorithms officer at Stitch Fix), Patrick Hayes (co-founder of SigOpt), and David Aronchick (co-founder of Bacalhau and Expanso and co-founder of Kubeflow).

FAQ

What are recommender systems?

Great questions! Recommender systems are the code and logic that power the songs you listen to on Spotify, or the movies you watch on Netflix. The most sophisticated recommender systems are complex machine learning-based systems maintained by hundreds of engineers and data scientists.

Do I need to know what recommender systems are to work at Rubber Ducky Labs?

If you don't have prior experience, we hope that you are eager to learn! Check the job listings for more specifics on what we're looking for.