Staff Machine Learning Engineer - Core Engineering

With more than 600 million users around the world and 400 billion ideas saved Pinterest Machine Learning engineers build personalized experiences to help Pinners create a life they love. With just over 4000 global employees our teams are small mighty and still growing. At Pinterest you’ll experience hands-on access to an incredible vault of data and contribute large-scale recommendation systems in ways you won’t find anywhere else.

We are seeking talented Staff Machine Learning Engineers for multiple openings across our Core Engineering organization including teams such as Search Notifications and Content & User Engineering. In these roles you will drive the development of state-of-the-art applied machine learning systems that power core Pinterest experiences.

What you’ll do:

• Design features and build large-scale machine learning models to improve user ads action prediction with low latency

• Develop new techniques for inferring user interests from online and offline activity

• Mine text visual user signals to better understand user intention

• Work with product and sales teams to design and implement new ad products

What we’re looking for:

• Degree in computer science machine learning statistics or related field

• 6+ years of industry experience building production machine learning systems at scale data mining search recommendations and/or natural language processing

• 2+ years of experience leading projects/teams

• Strong mathematical skills with knowledge of statistical methods

• Cross-functional collaborator and strong communicator

• Background in computational advertising is preferred but not required

In-Office Requirement Statement:

• We let the type of work you do guide the collaboration style. That means we're not always working in an office but we continue to gather for key moments of collaboration and connection.

Relocation Statement:

• This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

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