Pinterest

Machine Learning Engineer II at Pinterest focused on building and improving ML models for core product surfaces like Homefeed, Ads, Growth, Shopping, and Search. The role involves developing cutting-edge deep learning and ML technologies for personalization, using data-driven methods, and working with large-scale systems and stream data. The primary output is the improvement and deployment of ML models within the consumer product.

Machine Learning Engineer II at Pinterest focused on developing and implementing ML models for user targeting and personalization to drive engagement and growth. The role involves building scalable ML pipelines, conducting A/B tests, analyzing data, and contributing to ML infrastructure, with an emphasis on leveraging AI tools for engineering practices.

Director of Machine Learning Engineering for the Growth organization at Pinterest. This role will define the long-term ML vision, strategy, and operating model to drive user and engagement growth. Responsibilities include overseeing multiple ML teams, partnering with leadership on strategy, defining ML architecture, building and developing teams, and acting as a thought partner. The role requires deep expertise in building and operating large-scale ML systems in production, leading multiple ML teams, and defining technical strategies.

Manager II, Machine Learning-Search at Pinterest, leading a team responsible for major areas of the search engine, including indexing, ranking, query understanding, personalization, ML-based retrieval, shopping, videos, and infrastructure efficiency. The role involves innovative applications of NLP and Vision Models for query recommendations, multimodal search, agentic search, and query-based module generation. Requires experience leading large-scale production ML systems and applied ML experience.

Staff Data Scientist at Pinterest focused on the Engagement Ecosystem. The role involves developing and productionizing ML frameworks for forecasting, recommendation, and causal inference, applying advanced ML and generative AI techniques to model complex interactions, and championing responsible AI use. Requires expertise in ML, causal inference, experimentation, and large-scale data environments, with a track record of influencing product decisions and mentoring junior scientists.

Manager II, Machine Learning Engineering for Core Engineering at Pinterest, focusing on search and recommendations for over 500 million users. The role involves leading a team of ML engineers, driving technical direction, strategic planning, and execution for large-scale, low-latency systems and state-of-the-art ML models that deliver significant impact to pinners and business metrics.

Machine Learning Engineer at Pinterest focused on building and improving ML models for various product surfaces like Homefeed, Ads, Growth, Shopping, and Search. The role involves using data-driven methods to enhance candidate retrieval and working with large-scale recommendation systems.

Machine Learning Engineer at Pinterest focused on building and improving ML models for various product surfaces like Homefeed, Ads, Growth, Shopping, and Search, leveraging deep learning and large-scale data to personalize user experiences.

Machine Learning Engineer focused on Monetization at Pinterest, building and improving ML models for various product surfaces like Homefeed, Ads, Growth, Shopping, and Search. The role involves developing cutting-edge deep learning and ML technology for personalization, leveraging data for candidate retrieval, and enhancing content understanding with LLMs. Experience with large-scale recommender systems, ads ranking, and big data technologies is required.

Machine Learning Engineer focused on personalization and monetization within the consumer domain, specifically for Pinterest's ad platform. The role involves building and improving ML models for various product surfaces, leveraging data for candidate retrieval, and enhancing content understanding with LLMs. It requires end-to-end experience with ML systems and a practical understanding of recommender systems or ad ranking.