About the job

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.

You will have the opportunity to develop and implement algorithms that leverage user activity and interests to customize Search rankings. You will learn Machine Learning (ML) techniques exploring into advanced machine learning algorithms and approaches for the team, including natural language processing, user modeling, and ranking improvement. You will Contribute to an important component of search that impacts millions of users.

In Google Search, we're reimagining what it means to search for information - any way and anywhere. To do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful experience that people around the world rely on. In joining the Search team, you'll have an opportunity to make an impact on billions of people globally.

Responsibilities

• Design and analyze Search ranking algorithms and architecture, leveraging AI (e.g. Large Language Models, Deep Neural Networks) and C++, aimed at improving Search ranking results.
• Interface with Product Managers and other teams/engineers to develop and implement solutions.
• Train and debug machine learning models that power current and future search ranking solutions, specially directed at personalizing the user experience within search.
• Analyze experimental results and help decide deployment state for projects.