Minimum qualifications:

• Bachelor's degree or equivalent practical experience
  
• 8 years of experience in software development, and with data structures/algorithms.
  
• 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
  
• Experience in Machine Learning or Artificial Intelligence.

Preferred qualifications:

• 5 years of experience coding in one or more programming languages (e.g., C++, Python).
• Experience with Search Quality, ML, or Language Modeling.

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.

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 search quality (e.g., retrieval, ranking, triggering, result-presentation) systems for solving user needs that could be specified using long natural language queries.
  
• Build/tune large language models to distill to more serveable ranking/retrieval models, synthetic query generation to work well for queries not currently in the training data, optimizing for latency/capacity, multi-modality, and more.
  
• Drive query growth by making Google Local Search work better for user needs.