Staff Machine Learning Engineer - Ads Conversion Modeling
Within the Ads Ranking team, we try to connect the dots between the aspirations of Pinners and the products offered by our partners. In this role, you will be responsible for developing and executing a vision for the evolution of the machine learning technology stack for the conversion modeling team. You will work on tackling new challenges such as user sequence modeling, embedding features, model quantization, utility alignment, RoAS optimization and many more to advance the ML models that power the heavy ranker stage and delivery that bring together pinners and partners in this unique marketplace.
What you’ll do:
Lead the tech direction and responsible for the development of state-of-the-art applied machine learning projects for the ads conversion modeling team
Coach and mentor engineers for the conversion modeling team
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 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:
MS or PhD degree in Computer Science, 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
Relocation Statement:
This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.
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.
This role will need to be in the office for in-person collaboration 1-2 times/month and therefore can be situated anywhere in the country.
#LI-NM4 #LI-REMOTE
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Staff Machine Learning Engineer - Ads Conversion Modeling
Within the Ads Ranking team, we try to connect the dots between the aspirations of Pinners and the products offered by our partners. In this role, you will be responsible for developing and executing a vision for the evolution of the machine learning technology stack for the conversion modeling team. You will work on tackling new challenges such as user sequence modeling, embedding features, model quantization, utility alignment, RoAS optimization and many more to advance the ML models that power the heavy ranker stage and delivery that bring together pinners and partners in this unique marketplace.
What you’ll do:
Lead the tech direction and responsible for the development of state-of-the-art applied machine learning projects for the ads conversion modeling team
Coach and mentor engineers for the conversion modeling team
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 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:
MS or PhD degree in Computer Science, 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
Relocation Statement:
This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.
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.
This role will need to be in the office for in-person collaboration 1-2 times/month and therefore can be situated anywhere in the country.
#LI-NM4 #LI-REMOTE