Machine Learning Engineer, Shopping Discovery

Toronto, ON, CA
Regular
Engineering
1374465
Two women posing in front of a green screen.
Two women sitting at a table in a group setting.
Decorative light letters that spell PINS with a woman sitting on the floor next to them.
View of conference attendee from behind wearing a grey hat.
Decorative light letters that spell PINS.
A lush office patio with furniture overlooking a neighborhood in the city.
Various flyers for women's groups at Pinterest.
A view of Pinterest Toronto office common area.
A colorful art installation.

About Pinterest:  

Millions of people across the world come to Pinterest to find new ideas every day. It’s where they get inspiration, dream about new possibilities and plan for what matters most. Our mission is to help those people find their inspiration and create a life they love. In your role, you’ll be challenged to take on work that upholds this mission and pushes Pinterest forward. You’ll grow as a person and leader in your field, all the while helping Pinners make their lives better in the positive corner of the internet.

What is this team about?

Shopping is at the core of Pinterest’s mission to help people create a life they love. Pinterest has the unique advantage to build the world’s most inspirational, visual and personalized shopping experience for its 450M+ users worldwide. The shopping discovery team is in charge of helping Pinners to discover the most relevant products that they will love. The team works on shopping content recommendations and distribution on various surfaces e.g. product detail page, search, home feed, board etc. 

As an engineer of the team you will be working on the most cutting edge ML technologies in the area of recommender systems, embedding based candidate retrieval, personalization, whole page optimization, bandit algorithms, reinforcement learning etc. You’ll be running experiments and directly improving the shopping metrics contributing to the bottom line of the company. If you are excited about large scale machine learning problems in the area of recommendation, search and whole page optimization then you must consider this role.

What will this person do?

  • Develop large scale shopping recommendation algorithms
  • Build data pipelines to do data analysis and collect training data
  • Train deep learning models to improve quality and engagement of shopping recommenders
  • Work on backend and infrastructure to build, deploy and serve machine learning models
  • Develop ML algorithms to balance different objectives and model long term values
  • Drive the roadmap for next generation of shopping recommenders

What type of experience do they NEED to have?

  • 3+ years working experience in the area of applied Machine Learning
  • Interest or experience working on a large-scale search, recommendation and ranking problems
  • Interest and experience in doing full stack ML, including backend and ML infrastructure
  • Experience with big data technologies MapReduce/Hadoop/Hive/Presto/Spark
  • Expert in Java, C++ or Python

What skills are ideal but not required?

  • Ph.D. in an area of Machine Learning
  • Experience with large scale Whole page Optimization, Search or Recommendation algorithms
  • Domain expertise in Shopping

What makes this role special/exciting?

  • Shopping is cross-cutting, touches all aspects of Pinterest, so a wide variety of ML problems
  • Largely green-field so lots of opportunity
  • Huge impact – shopping is one of the major expansion areas for Pinterest

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