Machine Learning Engineer, Knowledge Pinterest
San Francisco, CA

Job Description

Pinterest helps you discover and do the things you love. We have more than 200M monthly active users who actively curate an ecosystem of more than 100B pins (ideas) on more than 1B boards, creating a rich human curated graph of immense value.

Technically, we are building out an internet scale personalized recommendation engine in 22+ languages, which requires a deep understanding of the users and content on our platform. As an engineer on the Pin Knowledge team, you'll work on content classification, user modeling, personalization and ranking. Engineers of this team often make measurably positive impact on hundreds of millions of users with improved machine learning modeling and featurization breakthroughs.

Example projects of the team include:

1) Building end-to-end ML pipeline to discover the best text annotations for a Pin, and use them to improve Homefeed and Ads top-line metrics

2) Develop state-of-the-art text embedding signals, as well as other featurization techniques for better model prediction performance

3) Analyze, compare and implement LR, GBDT and DNN models that are capable to serve our prod traffic in near real-time

4) Closely work with the other product teams at Pinterest (Homefeed, Search, Ads etc) and conduct A/B experiments to improve various top-line metrics such as user engagement and revenue.

What you'll do:

* Work with a group of friendly and experienced ML engineers to build the next generation ML signal pipeline widely used in Pinterest, including candidate generation, featurization and ranking model improvement.
* Design and build systems that combine machine learning and product design to continuously improve over time.
* Partner closely with other product teams across the organization to experiment with different algorithms and validate their effectiveness, while gaining knowledge of how ML works in all these products.

What we're looking for:

* 5+ years of software engineering/ML expertise and the ability to build scalable systems
* Knowledge of algorithms, data-structures and measurement/statistics.
* Practical experiences in machine learning, natural language processing or information retrieval
* Experience working with large code bases, cross team collaboration, mentoring other engineers, giving and getting feedback, and reviewing code/systems.
* Experience with MapReduce/Hadoop and/or distributed systems.