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Manager II, Machine Learning - Ads Retrieval

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.

Creating a life you love also means finding a career that celebrates the unique perspectives and experiences that you bring. As you read through the expectations of the position, consider how your skills and experiences may complement the responsibilities of the role. We encourage you to think through your relevant and transferable skills from prior experiences.

Our new progressive work model is called PinFlex, a term that’s uniquely Pinterest to describe our flexible approach to living and working. Visit our PinFlex landing page to learn more. 

Lead and empower a team of talented Machine Learning Engineers within the Ads Retrieval Team at Pinterest, driving the innovation and execution of our global Shopping Ads platform. As a Manager II, Machine Learning, you will be instrumental in shaping the technical vision and strategy for the next generation of ads retrieval models and scalable infrastructure. You will guide your team in pioneering advancements across cutting-edge technologies vital to our advertising ecosystem, including Generative Retrieval, User Sequence Modeling, Learning to Rank, and large-scale Approximate Nearest Neighbor (ANN) techniques. You will oversee the team's efforts in tackling challenges at immense scale – managing a 5 billion+ shopping ads index – and ensure we leverage the most efficient techniques to deliver exceptional performance. This is a high-impact leadership opportunity to shape the future of Pinterest Shopping Ads, directly impacting user experience and advertiser success in a unique discovery-driven marketplace.

What you’ll do:

  • Lead and mentor a team of Machine Learning Engineers: Provide technical guidance, mentorship, and career development for a team focused on designing, implementing, and scaling next-generation retrieval models for Shopping Ads. Foster a collaborative and high-performing team culture.
  • Define and drive the technical vision and strategy for Ads Retrieval: Collaborate with Product, Data Science, and Engineering leadership to establish a clear roadmap for innovation in retrieval models and infrastructure, aligning with the overall Pinterest Shopping Ads strategy.
  • Oversee the design and implementation of advanced retrieval models: Guide the team in pioneering advanced architectures beyond traditional approaches, leading the implementation and optimization of Generative Retrieval, User Sequence Modeling, and Learning-to-Rank models to significantly enhance ad relevance, capture user intent, and improve ranking quality.
  • Direct the development and optimization of massively scalable and efficient Ads Retrieval infrastructure: Lead the evolution of our next-gen infrastructure, ensuring it can handle a 5 billion+ Shopping Ads index with lightning-fast, cost-effective retrieval through techniques like efficient ANN algorithms, GPU-accelerated systems, and embedding quantization.
  • Champion innovation in personalized Shopping Ads recommendations: Steer the team in developing hyper-personalized retrieval models that incorporate user sequence modeling, learning-to-rank, and generative retrieval to surface the most relevant and novel ads, continuously pushing the boundaries of personalization.
  • Foster a holistic approach to retrieval excellence: Evaluate and advocate for the integration of cutting-edge technologies, including Large Language Models (LLMs), Generative Retrieval techniques, advanced Sequence Models, and efficient ANN algorithms, to continuously revolutionize Shopping Ads retrieval and enhance relevance, efficiency, and user engagement.
  • Collaborate cross-functionally at a leadership level: Partner closely with Product Management, Data Science, and other Engineering teams to holistically improve the user journey, optimize ad performance across all stages of retrieval and ranking, and drive demand-side growth for Shopping Ads, ensuring a balanced approach across different modeling and infrastructure innovations.
  • Drive technical decision-making and ensure engineering best practices: Establish and uphold high standards for code quality, system design, and operational excellence within the team.

What we’re looking for:

  • MS or PhD in Computer Science, Statistics, or related field with a strong foundation in machine learning and information retrieval, and deep understanding of a range of retrieval modeling techniques.
  • 8+ years of industry experience architecting, building, and scaling large-scale production recommendation or search systems, with a significant focus on high-performance retrieval leveraging diverse modeling approaches, including experience leading technical teams.
  • Deep expertise in recommendation systems, especially large-scale retrieval algorithms and architectures, encompassing Generative Retrieval, User Sequence Modeling, Learning-to-Rank, and efficient ANN techniques.
  • Mastery of deep learning techniques and a proven track record of optimizing model performance for complex retrieval tasks in large-scale environments, across various model types including generative, sequence-based, and ranking models.
  • Demonstrated ability to lead and grow high-performing engineering teams, providing technical vision, guidance, and mentorship. Experience managing complex technical projects across multiple areas of retrieval innovation and driving balanced technological advancements.
  • Excellent communication and cross-functional collaboration skills, capable of articulating complex technical visions to both technical and non-technical audiences, building consensus across diverse teams, and influencing at a leadership level, representing a comprehensive understanding of various retrieval technologies.
  • Hands-on experience developing and deploying recommendation systems utilizing Generative Retrieval, User Sequence Modeling, and/or Learning-to-Rank techniques, with experience guiding teams in these areas.
  • Expertise in computational advertising, particularly within Shopping Ads or e-commerce domains, with a broad understanding of different retrieval modeling paradigms and their impact on business outcomes.
  • Proven track record of optimizing GPU-based systems for high-throughput, low-latency retrieval and experience in implementing embedding quantization and other efficiency techniques at scale, with experience leading teams in these efforts.
  • Familiarity with a wide range of retrieval efficiency and scaling techniques, including efficient ANN algorithms, token-based retrieval, and embedding quantization, and the ability to guide a team in leveraging these techniques effectively.

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 once a month, and therefore needs to be in a commutable distance from one of the following offices: San Francisco, Palo Alto, Seattle.

 

This position is not eligible for relocation assistance.

 

 

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At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.

Information regarding the culture at Pinterest and benefits available for this position can be found here.

US based applicants only
$208,145$364,254 USD

Our Commitment to Inclusion:

Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, religion or religious creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, age, marital status, status as a protected veteran, physical or mental disability, medical condition, genetic information or characteristics (or those of a family member) or any other consideration made unlawful by applicable federal, state or local laws. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require a medical or religious accommodation during the job application process, please complete this form for support.
 

Average salary estimate

$286199.5 / YEARLY (est.)
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$208145K
$364254K

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What You Should Know About Manager II, Machine Learning - Ads Retrieval, Pinterest

At Pinterest, we’re on a mission to inspire people everywhere to create the life they love, and we want you to join us as a Manager II, Machine Learning for our Ads Retrieval Team in San Francisco, Palo Alto, or Seattle! This leadership role isn’t just about managing a talented team of Machine Learning Engineers, although you'll definitely excel at that. You’ll be at the forefront of innovation, shaping the technical vision and strategy for our next-gen ads retrieval models and infrastructure. You'll oversee high-impact projects that tackle big challenges, like managing an extensive index of over 5 billion shopping ads, ensuring optimal performance through advanced techniques like Generative Retrieval and Learning to Rank. You’ll guide your team as they create hyper-personalized ad experiences that resonate with our users, driving engagement and success for advertisers. We're looking for someone with a robust background in machine learning and information retrieval, ideally with at least 8 years of industry experience. Your expertise will not just enhance the team's skills; it'll also push boundaries and educate others on the latest advancements in retrieval technologies. Collaboration is key in this role—you’ll work closely with Product Management, Data Science, and Engineering to ensure a holistic approach to the user journey. Plus, our new PinFlex approach means you can enjoy a blend of flexible and in-office work to connect and collaborate with your team. Join us at Pinterest and help redefine how users connect with ideas and products!

Frequently Asked Questions (FAQs) for Manager II, Machine Learning - Ads Retrieval Role at Pinterest
What are the primary responsibilities of the Manager II, Machine Learning position at Pinterest?

As the Manager II, Machine Learning at Pinterest, you will lead and mentor a team of talented Machine Learning Engineers focusing on the Ads Retrieval Team. Your key responsibilities will include defining technical vision and strategy for advanced retrieval models, overseeing the development of scalable infrastructure for shopping ads, and championing innovation in personalized ad recommendations through cutting-edge techniques such as Generative Retrieval and Learning to Rank.

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What qualifications are needed for the Manager II, Machine Learning role at Pinterest?

To qualify for the Manager II, Machine Learning position at Pinterest, you should hold an MS or PhD in Computer Science, Statistics, or a related field, with at least 8 years of industry experience in machine learning and retrieval systems. Expertise in recommendation systems, generative modeling, and the ability to lead high-performing teams is essential, along with strong communication skills to collaborate effectively across various teams.

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How does the Manager II, Machine Learning position contribute to the success of Pinterest's Ads Retrieval Team?

The Manager II, Machine Learning role contributes to Pinterest's Ads Retrieval Team by driving innovation and technical excellence within the ads infrastructure. You will directly impact user experience and advertiser success by guiding advanced retrieval model development, optimising performance on a massive scale, and delivering personalized recommendations that enhance engagement and relevance.

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What growth opportunities are available for a Manager II, Machine Learning at Pinterest?

As a Manager II, Machine Learning at Pinterest, joining a cutting-edge technology team provides extensive growth opportunities. You will have the chance to lead pioneering projects in machine learning while enhancing your leadership skills in mentorship and team management. Additionally, you'll engage in cross-functional collaboration and stay at the forefront of industry advancements in retrieval technologies, allowing you to expand your expertise significantly.

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What is the work environment like for the Manager II, Machine Learning position at Pinterest?

Pinterest promotes a collaborative and inclusive work environment. As a Manager II, Machine Learning, you will benefit from the PinFlex model, which allows flexibility in work arrangements. While this role includes in-person collaboration once a month, it also embraces a dynamic approach to team interactions, ensuring both collaboration and individual work flexibility.

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Common Interview Questions for Manager II, Machine Learning - Ads Retrieval
Can you explain your experience with machine learning algorithms and their application in retrieval systems?

When answering this question, focus on specific algorithms you have used, such as Learning to Rank or Generative Retrieval techniques. Discuss your hands-on experience with building and optimizing systems, particularly in high-throughput environments. Use examples to illustrate your strengths and show how these techniques can significantly impact ad relevance and user engagement.

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How do you prioritize technical projects within a team?

A good approach is to highlight your ability to assess project impact based on team capabilities and strategic goals. Discuss how you work collaboratively with stakeholders to align technical initiatives with overall business objectives, ensuring that the team focuses on the most impactful projects first.

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Describe a complex technical challenge you've faced and how you addressed it.

Use a specific example related to machine learning or retrieval systems and detail the steps you took to understand the challenge, the resources you utilized, and the innovative solutions you implemented. Emphasize your analytical skills and collaboration with team members to reach a successful resolution.

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What strategies do you use for mentoring and developing your team members' skills?

Discuss your approach to mentorship, emphasizing one-on-one sessions, constructive feedback, and creating opportunities for team members to take ownership of projects. Highlight the importance of fostering a growth mindset and encouraging continuous learning within the team.

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How would you ensure efficient retrieval of ads on a large scale?

Addressing efficiency requires a focus on optimizing algorithms and infrastructure. You could discuss using advanced techniques like embedding quantization and GPU-accelerated systems, culminating in a robust architecture that delivers fast and cost-effective ad retrieval while handling large datasets effectively.

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Explain your understanding of personalization techniques in ads retrieval.

Discuss specific personalization strategies such as User Sequence Modeling and Learning-to-Rank. Highlight how these techniques help create tailored ad experiences that enhance both user engagement and conversion rates, showcasing your knowledge of user behavior insights.

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How do you plan to manage cross-functional collaboration effectively?

Emphasize the importance of clear communication, setting mutual goals, and aligning stakeholders through regular check-ins and updates. Describe how you would facilitate collaboration amongst Product Management, Data Science, and Engineering to ensure initiatives progress smoothly.

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What role does data play in your decision-making process?

Explain how you rely on data analysis to inform project prioritization, assess the effectiveness of models, and identify areas for improvement. Highlight your experience in transforming data insights into actionable strategies to achieve desired goals.

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How do you stay updated on advances in machine learning and retrieval technologies?

Mention the importance of continuous learning by following industry publications, participating in conferences, and engaging with professional networks. Share examples of recent advancements that intrigue you and how they might apply at Pinterest.

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Why do you want to work at Pinterest specifically?

Focus on Pinterest's mission of inspiring people and how the work aligns with your professional values and expertise in machine learning. Discuss the company's commitment to innovation and inclusivity, and express your enthusiasm for contributing to a brand that connects users with ideas and products.

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Our mission is to bring everyone the inspiration to create a life they love.

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April 12, 2025

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