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# Sr. Machine Learning Engineer, EPD Ads

- **Company:** Pinterest
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $189,721.0 - $332,012.0
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Big Data, Cursor, Software Debugging, Apache Hadoop, Machine Learning, Natural Language Processing, Recommender Systems, Microsoft Copilot, SQL Databases, Reinforcement Learning, Large Language Models, Apache Spark, AI Coding Agents, Information Technology, Machine Learning Operations, Feature Extraction, Code Restructuring, Data Pipelines
- **Published:** October 8, 2026
- **Apply:** https://startup.jobs/sr-machine-learning-engineer-epd-ads-pinterest-5707161

## About the Role

* 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
 * Nice to have:
 + 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

## Description

* 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