About this role
Summary: Design and optimize DeepRank, a complex system for ranking web results, leveraging Gemini AI models. The role involves building end-to-end ML systems, managing project priorities, and collaborating with cross-functional teams to improve search quality and relevance at massive scale.
info_outline XIn most instances, this position requires in-person interviews as part of the hiring process. Minimum qualifications: Bachelor’s degree or equivalent practical experience. 5 years of experience with software development in one or more programming languages. 3 years of experience with Natural Language Processing (NLP) concepts, algorithms, and experience in designing NLP solutions. 3 years of experience with deep learning, machine learning, and large-scale data processing. 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture. 3 years of experience with end-to-end Machine Learning infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging). Preferred qualifications: Master's degree or PhD in Computer Science, or related technical field. 5 years of experience with data structures and algorithms. 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects. 2 years of experience with AI Algorithms, Machine Learning Algorithms, Search Quality, and Search Infrastructure. 1 year of experience in a technical leadership role. Experience developing accessible technologies.
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. We're seeking an engineer to contribute to DeepRank, one of the most impactful systems in Google Search, responsible for ranking web results. In this role, you will be involved in designing and optimizing this complex system. A key aspect of this role is to harness the newly available capabilities of Gemini, Google's advanced AI models, to push the boundaries of search relevance and quality. This is part of an exciting transition to build the next generation of DeepRank on the modern Gemax Prod framework. With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions. Join Google Search AI and work at the intersection of industry leading global scale. We apply breakthrough research, leveraging models like Gemini, to launch high-impact products that help billions ask any question and get things done. If you want to drive the future of AI in Search and work with world-class experts to deliver tangible, global impact and continuous innovation, this is the place.Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google.
Responsibilities Write and test product or system development code. Address research and engineering problems at the edge of GenAI with potential for immediate real-world user impact at massive scale for current and emergent query streams. Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies. Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback. Conduct applied research figuring out research developments best suited to solve open-ended problems. Build end-to-end ML systems spanning data, modeling, evaluation, and serving and search scale. Build a comprehensive understanding of the search stack, evaluation metrics, query sets, and philosophy of improving search quality and presentation. Partner closely with appropriate teams throughout Search, GDM, Research, Core ML, and Platforms.