Software Engineer, Semantic Understanding, Search Ads Personalization

Write and test product or system development code.Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies.Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).Build model and architectural components that provide effective attention for ads over user preferences and history.Design real-time infrastructure and representations of user viewed content.Minimum qualifications:Bachelor’s degree or equivalent practical experience.2 years of experience programming in Python or C++.1 year of experience building and deploying recommendation systems models (retrieval, prediction, ranking, personalization, search quality, embedding) in production.1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging).Experience with Large Language Model (LLMs), Multi-Modal, or Large Vision Modeling.Preferred qualifications:Master's degree or PhD in Computer Science, or a related technical field.2 years of experience with data structures and algorithms.Experience developing and optimizing machine learning algorithms for personalization, ranking, and content recommendation.Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward. Semantic understanding is the ability to comprehend the actual meaning, intent, and context of words, symbols, or language rather than just recognizing individual keywords or surface-level grammar. Our personalization models are only as good as our understanding of the content users interacted with.In this role, you will will focus on end-to-end enriching personalization stack with real-time understanding of user interactions with content pieces (for eg. what was the content of the page or video? How can we instantaneously bring in rich information as features for our personalization models to adapt to the user's emergent interests).Your responsibilities include collaborating across modeling and infrastructure teams across Ads, to build real-time infrastructure and representations of user viewed content, as the primary growth driver of our impact and accuracy.Google Ads is at the forefront of AI innovation, applying cutting-edge machine learning and Generative AI models like Gemini to power a multi-billion dollar global business.Our work directly impacts billions of users by protecting users from harm, improving ad quality, and optimizing campaigns for advertiser return-on-investment. We foster a culture of deep collaboration, partnering closely with teams like Google Research and DeepMind to solve complex challenges. Join us to work on state-of-the-art AI, take on problems at an unparalleled scale, and build the next generation of advertising technology.Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $147000 - $210000 (USD) + 15% bonus target + equity + benefitsLearn more about benefits at Google.Bachelor’s degree or equivalent practical experience.2 years of experience programming in Python or C++.1 year of experience building and deploying recommendation systems models (retrieval, prediction, ranking, personalization, search quality, embedding) in production.1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging).Experience with Large Language Model (LLMs), Multi-Modal, or Large Vision Modeling.