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Sr. Staff Machine Learning Engineer - Monetization, Privacy ML

Pinterest

Full-time
USA
$213k-$438k per year
machine learning
engineer
python
computer science
processing
Apply for this position

At Pinterest, we treat user privacy as our first priority in our product design and ads businesses. Within the monetization ML team, we try to connect the dots between the aspirations of Pinners and the products offered by our partners, while respecting users’ privacy choices.

In this role, you will be guiding the technical directions, leading the development, and advancing the state of the art privacy-preserved ML solutions that power the ads engagement and delivery which bring together Pinners and partners in this unique marketplace. You will influence and collaborate with cross functional teams to drive privacy initiatives, ensure compliance with relevant regulations and enable effective advertising strategies.

What you'll do:

  • Lead projects that involve developing and deploying state of the art (and beyond) privacy-preserved ML models in production ads delivery systems.

  • Collaborate and partner with cross functional teams across the company, to help define and drive forward-looking privacy-preserved ML strategy for the team and across the company.

  • Be hands-on and develop models and systems that lead to step-function improvements to our topline metrics.

  • Mentor and grow junior ML engineers in ads quality and partner teams and help to uplevel ML talent across the company.

 

What we're looking for:

  • Experience in privacy preserving machine learning techniquesDeep practical knowledge of large scale recommender systems, or ads deliver funnelsExperience in deep learning, transformers, deep cross network, etc. Hands-on experience training and applying models at scale using deep learning frameworks like PyTorch or Tensorflow.

  • Experience with large scale data processing (e.g. Hive, Scalding, Spark, Hadoop, Map-reduce).

  • 8+ years working experience in the engineering teams that build large-scale ML-driven user-facing products.

  • 3+ years experience leading cross-team engineering efforts.

  • Understanding of an object-oriented programming language (Java, C++, Python, etc).

  • Experience in working on backend and ML systems for large-scale user-facing products, and have a good understanding of how they all work is a plus.

  • Bachelor’s/Master’s degree in a relevant field such as Computer Science, or equivalent experience.

 

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 time per week and therefore needs to be in a commutable distance from one of the following offices: San Francisco, Palo Alto, or Seattle. 

 #LI-NM4

#LI-REMOTE

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About the job

Full-time
USA
$213k-$438k per year
4 Applicants
Posted 12 hours ago
machine learning
engineer
python
computer science
processing

Apply for this position

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Sr. Staff Machine Learning Engineer - Monetization, Privacy ML

Pinterest

At Pinterest, we treat user privacy as our first priority in our product design and ads businesses. Within the monetization ML team, we try to connect the dots between the aspirations of Pinners and the products offered by our partners, while respecting users’ privacy choices.

In this role, you will be guiding the technical directions, leading the development, and advancing the state of the art privacy-preserved ML solutions that power the ads engagement and delivery which bring together Pinners and partners in this unique marketplace. You will influence and collaborate with cross functional teams to drive privacy initiatives, ensure compliance with relevant regulations and enable effective advertising strategies.

What you'll do:

  • Lead projects that involve developing and deploying state of the art (and beyond) privacy-preserved ML models in production ads delivery systems.

  • Collaborate and partner with cross functional teams across the company, to help define and drive forward-looking privacy-preserved ML strategy for the team and across the company.

  • Be hands-on and develop models and systems that lead to step-function improvements to our topline metrics.

  • Mentor and grow junior ML engineers in ads quality and partner teams and help to uplevel ML talent across the company.

 

What we're looking for:

  • Experience in privacy preserving machine learning techniquesDeep practical knowledge of large scale recommender systems, or ads deliver funnelsExperience in deep learning, transformers, deep cross network, etc. Hands-on experience training and applying models at scale using deep learning frameworks like PyTorch or Tensorflow.

  • Experience with large scale data processing (e.g. Hive, Scalding, Spark, Hadoop, Map-reduce).

  • 8+ years working experience in the engineering teams that build large-scale ML-driven user-facing products.

  • 3+ years experience leading cross-team engineering efforts.

  • Understanding of an object-oriented programming language (Java, C++, Python, etc).

  • Experience in working on backend and ML systems for large-scale user-facing products, and have a good understanding of how they all work is a plus.

  • Bachelor’s/Master’s degree in a relevant field such as Computer Science, or equivalent experience.

 

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 time per week and therefore needs to be in a commutable distance from one of the following offices: San Francisco, Palo Alto, or Seattle. 

 #LI-NM4

#LI-REMOTE

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