> Markdown version of [/jobs/ext/1208360-sr-machine-learning-engineer-monetization-engineering](https://www.wearedevelopers.com/jobs/ext/1208360-sr-machine-learning-engineer-monetization-engineering). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Sr. Machine Learning Engineer, Monetization Engineering

- **Company:** Pinterest
- **Location:** Seattle, WA, United States
- **Experience:** Expert
- **Salary:** $189,721.0 - $332,012.0
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Big Data, Cursor (Graphical User Interface Elements), Software Debugging, Apache Hadoop, Machine Learning, Natural Language Processing, Recommender Systems, SQL Databases, Reinforcement Learning, Large Language Models, Apache Spark, Information Technology, Machine Learning Operations, Code Restructuring, Data Pipelines
- **Published:** July 8, 2026
- **Apply:** https://diversityjobs.com/main/sendform/8/8/28176/1/8519432?backUrl=%2Fcareer%2F8519432%2FSr-Machine-Learning-Engineer-Monetization-Engineering-Washington-Seattle

## 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


## Related Videos

- [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data)
- [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail)
- [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology)
- [How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)](https://www.wearedevelopers.com/videos/752-how-we-built-a-machine-learning-based-recommendation-system-and-survived-to-tell-the-tale)
- [Let's Get Aggregated: Custom UDAFs in Spark  ](https://www.wearedevelopers.com/videos/1649-let-s-get-aggregated-custom-udafs-in-spark)
- [Why and when should we consider Stream Processing frameworks in our solutions](https://www.wearedevelopers.com/videos/1085-why-and-when-should-we-consider-stream-processing-frameworks-in-our-solutions)

## Related Articles

- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)
- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)
- [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)
- [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)
- [Dev Digest 137 - AI'm not sure about this](https://www.wearedevelopers.com/magazine/485-dev-digest-137-ai-m-not-sure-about-this)
- [Dev Digest 129 - Now that's what I call private data!](https://www.wearedevelopers.com/magazine/468-dev-digest-129-now-that-s-what-i-call-private-data)