PhD University Grad Machine Learning Engineer
PhD University Grad Machine Learning Engineer
Company: Pinterest
Location: Toronto, ON
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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.
As a Machine Learning Engineer at Pinterest, you'll work on tackling new challenges in machine learning and artificial intelligence. We are building the world's first discovery engine, serving up millions of recommendations to an incredibly loyal user base. At the same time, our teams are building one of the fastest growing online ad platforms, and our success depends on mining rich user interest data that helps us connect users with highly relevant advertisers. In this role, you can join a team in any of the following areas: image recognition, user modeling, recommender systems, natural language processing, and big data analytics. As you kickstart your career at Pinterest, you’ll help us maneuver through crazy growth and insane scale while pinpointing tomorrow’s engineering challenges.
What you’ll do:
- Apply machine learning approaches to build rich signals that enable ranking and product engineers to build deeper experiences to further engage Pinners
- Own, improve, and scale signals over both structured and unstructured content that bring tens of millions of rich content to Pinterest each day
- As a ML engineer, you will design and build large scale ML systems that can process billions of products
What we’re looking for:
- PhD in Machine Learning
- Strong ability to work cross-functionally and with partner engineering teams
- Experience in content modeling at consumer Internet scale
- Hands-on experience on large scale machine learning systems (full ML stack from modeling to deployment at scale.)
- Hands-on experience with big data technologies (e.g., Hadoop/Spark) and scalable realtime systems that process stream data
- Strong knowledge in Java, Scala or Python.
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