PhD University Grad Machine Learning Engineer 2026 (USA)

Pinterest is a platform that inspires millions to create a life they love. As a Machine Learning Engineer, you will work on cutting-edge research in machine learning and artificial intelligence, contributing to large-scale recommendation systems and tackling new challenges in the field. Responsibilities Contribute to cutting-edge research in machine learning and artificial intelligence that can be applied to Pinterest problems Collect, analyze, and synthesize findings from data and build intelligent data-driven model Write clean, efficient, and sustainable code Use machine learning, natural language processing, and graph analysis to solve modeling and ranking problems across discovery, ads and search Design, build, and test models to predict engagement for notifications (push, emails, in-app notifications) Build content recommendation systems to power our push, email, and in-app notifications Work on state-of-the-art large-scale applied machine learning projects Scope and independently solve moderately complex problems Skills PhD in Computer Science, ML, NLP, Statistics, Information Sciences or related field required Machine Learning experience (ranking, computer vision, NLP, content recommendations, embedding, information retrieval etc) Proficiency in at least one systems language (Java, C++, Python) or one ML framework (Tensorflow, Pytorch, MLFlow) Experience with big data technologies (e.g., Hadoop/Spark) and scalable realtime systems that process stream data Experience in research and in solving analytical problems Strong communicator and team player with the ability to find solutions for open-ended problems Publications in machine learning, AI, data science, data analytics, statistics, or related technical fields Interest in research and in applying ML to impactful real-world problems on the Pinterest product Company Overview Pinterest is a visual bookmarking tool for saving and discovering creative ideas. It was founded in 2010, and is headquartered in San Francisco, California, USA, with a workforce of 1001-5000 employees. Its website is