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Manager II – Machine Learning Engineering – Search Ranking & Blending
Company | Pinterest |
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Location | San Francisco, CA, USA |
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Salary | $176924 – $364254 |
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Type | Full-Time |
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Degrees | Master’s, PhD |
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Experience Level | Senior, Expert or higher |
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Requirements
- MS or PhD degree in Computer Science, Machine Learning, Statistics or related field, or equivalent experience.
- Proven experience in leading and managing large-scale production systems, such as search, recommendation, or advertising platforms, utilizing advanced machine learning and big data technologies.
- Strong background in applied machine learning; familiarity with recommendation systems is highly desirable.
- Demonstrated ability to define and drive the strategic roadmap for scalable, production-quality systems from concept to execution.
- Strong focus on product impact and user experience within a consumer-focused environment.
- Minimum of 2 years of experience managing a high-performing machine learning engineering team of 8+ members.
- 8+ years of experience in software development, with a proven track record of delivering impactful solutions.
Responsibilities
- Be responsible for the search ranking and blending area, a significant component of the search product at Pinterest.
- Deeply understand the Pinterest search product and drive the vision for the search ranking and blending team, ensuring the team’s work directly contributes to the company’s goals.
- Manage and mentor a team of Machine Learning engineers, providing guidance and support to help them grow their careers.
- Collaborate closely with other engineering teams at Pinterest to enhance the search experience for users, including Search Product, Infrastructure, research, and content signals.
- Provide visibility to senior leadership regarding the team’s impact.
- Partner with stakeholders across the company, including product management, data scientists, and design, to expand impact.
- Build a culture of excellence and expertise within the team.
Preferred Qualifications
- Familiarity with recommendation systems is highly desirable.