Sr. Machine Learning Engineer, Content Shopping

Responsibilities

• Identify and evaluate high-value content sources for Pinterest including websites, merchants, and social media accounts
• Help build scalable systems to acquire that content and extract structured attributes from it.
• Partner closely with cross-functional teams across Pinterest to improve content quality and power better user experiences, such as reducing low-quality content and improving search relevance.
• Train, fine-tune, and distill language models to better understand webpages and deploy those models in production at scale.
• Design and build systems for managing large-scale datasets, improving data quality, and automating model iteration and improvement.
• Use modern agentic coding tools to accelerate development, experimentation, and operational efficiency.

Requirements

• 5+ years of industry experience applying machine learning to real-world problems, such as search, ranking, recommender systems, natural language processing, personalization, reinforcement learning, or graph representation learning.
• Hands-on experience training, evaluating, and deploying language models in production environments.
• Strong problem-solving skills, with the ability to work autonomously, think creatively, and drive ambiguous projects forward.
• Experience or strong interest in web crawling, web scraping, and large-scale content acquisition.
• M.S. or Ph.D. in Machine Learning, Computer Science, or a related technical field.
• Publications in top-tier machine learning conferences.
• Passion for applied machine learning and for building products that improve the Pinterest experience.
• Experience with web crawling, web scraping, search, recommendation systems, or content understanding pipelines.
• Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring.
• Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration.
• Relocation Statement:
• This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.
• This job posting is for an open vacancy. Please note that the company utilizes artificial intelligence to screen applicants for the positions.
• #LI-REMOTE
• #LI-AK7
• Our Commitment to Inclusion:

Benefits

Additional Information

About Pinterest: Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we're on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other's unique experiences and embrace the flexibility to do your best work. Creating a career you love? It's Possible. At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we're looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we'll explore your foundational skills and how you collaborate with AI. Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here . With more than 535 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 3,500 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. The Content Shopping Mining ML team builds machine learning systems that understand shopping-related content across the web, turning unstructured merchant pages into high-quality structured product data like price, title, availability, and images. This helps improve product experiences on Pinterest, including content quality, distribution, recommendations, and search; for example, see the team's KDD 2025 paper, Cross-Domain Web Information Extraction https://arxiv.org/pdf/2508.01096 .

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