Machine Learning Engineer, Monetization Engineering

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Company Overview

A leading global visual discovery and inspiration platform connects hundreds of millions of users with ideas across a wide range of interests, helping people discover possibilities, plan projects, and create meaningful experiences. The organization combines advanced artificial intelligence, machine learning, and large-scale data infrastructure to deliver personalized experiences and help users discover relevant content.

With a global workforce of more than 4,000 employees and hundreds of millions of users, the organization provides machine learning engineers with opportunities to work with large-scale datasets and develop sophisticated recommendation systems. The company emphasizes innovation, collaboration, flexibility, and the responsible use of AI throughout its products and engineering practices.

The Monetization Machine Learning Engineering team focuses on connecting user interests and aspirations with products and solutions offered by advertising partners. The team is responsible for advancing machine learning technologies within advertising and developing systems that improve personalization, discovery, retrieval, ranking, and content understanding.

The organization is seeking a Machine Learning Engineer, Monetization Engineering to develop and execute the evolution of machine learning technology within its advertising ecosystem. This role will involve building advanced machine learning systems, improving recommendation and retrieval capabilities, and collaborating with teams across the organization to deliver personalized experiences.

Key Responsibilities

• Build cutting-edge machine learning technology using the latest advances in deep learning and machine learning to personalize user experiences.
• Partner with teams across the organization to experiment with and improve machine learning models across product surfaces such as Homefeed, Ads, Growth, Shopping, and Search.
• Develop a broad understanding of how machine learning is applied across different product areas.
• Use data-driven methods and leverage unique data characteristics to improve candidate retrieval systems.
• Develop and improve large-scale machine learning systems for advertising and personalization.
• Work in a high-impact engineering environment focused on rapid experimentation and product launches.
• Monitor and apply emerging industry trends in recommendation systems and machine learning.
• Leverage Large Language Models (LLMs) to improve content understanding.
• Contribute to the evolution of the machine learning technology stack supporting monetization and advertising products.
• Collaborate with engineering, product, and other technical teams to develop scalable machine learning solutions.

Required Qualifications

• 2+ years of industry experience applying machine learning methods such as user modeling, personalization, recommender systems, search, ranking, natural language processing, reinforcement learning, or graph representation learning.
• Bachelor’s degree in computer science, machine learning, statistics, a related technical field, or equivalent practical experience.
• End-to-end hands-on experience building data processing pipelines and large-scale machine learning systems.
• Experience working with big data technologies such as Hadoop or Spark.
• Practical knowledge of large-scale recommender systems, modern advertising ranking, retrieval, targeting, or marketplace systems.
• Strong understanding of machine learning principles and their practical application to large-scale products.
• Ability to collaborate effectively with cross-functional engineering and product teams.

Preferred Qualifications

• Publications at leading machine learning conferences.
• Experience using AI coding assistants such as Cursor, Copilot, Codex, or similar tools for development, debugging, testing, and refactoring.
• Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL and data exploration, and engineering workflow acceleration.
• Expertise in scalable real-time systems that process streaming data.
• Passion for applied machine learning and consumer technology products.
• Background in computational advertising.
• Experience working with recommendation systems and large-scale personalization technologies.

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