About the job
Within the Monetization ML Engineering team, we try to connect the dots between the aspirations of Pinners and the products offered by our partners. In this role, you will be responsible for developing and executing a vision for the evolution of the machine learning technology stack within Ads.
Responsibilities
Build cutting edge technology using the latest advances in deep learning and machine learning to personalize Pinterest
Partner closely with teams across Pinterest to experiment and improve ML models for various product surfaces (Homefeed, Ads, Growth, Shopping, and Search), while gaining knowledge of how ML works in different areas
Use data driven methods and leverage the unique properties of our data to improve candidates retrieval
Work in a high-impact environment with quick experimentation and product launches
Keep up with industry trends in recommendation systems
Leverage LLMs to enhance content understanding
Qualifications
Minimum
2+ years of industry experience applying machine learning methods (e.g., user modeling, personalization, recommender systems, search, ranking, natural language processing, reinforcement learning, and graph representation learning)
Degree in computer science, statistics, or related field; or equivalent experience
End-to-end hands-on experience with building data processing pipelines, large scale machine learning systems, and big data technologies (e.g., Hadoop/Spark)
Practical knowledge of large scale recommender systems, or modern ads ranking, retrieval, targeting, marketplace systems
Preferred
M.S. or PhD in Machine Learning or related areas
Publications at top ML conferences
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
Expertise in scalable realtime systems that process stream data
Passion for applied ML and the Pinterest product
Background in computational advertising