Software Engineer II, Search Ads Retrieval

Vor 6 Tagen

CH Zürich Google Switzerland GmbH Vollzeit

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.

At Google, our users come first, and the Systems Infrastructure team is at the heart of that promise. We build the technologies that transform the way we think about doing business. Whether working on our cloud systems, researching the latest in computer technology or keeping Google's internal systems humming, Googlers and users alike rely on us to keep things running. We're back-end experts: protecting your privacy and ensuring your security.

Minimum qualifications:
  • Bachelor's degree or equivalent practical experience.
  • 1 year of experience with data structures or algorithms.
  • Experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript).
Preferred qualifications:
  • Experience with natural language processing or machine learning.
  • Analyze available data about advertisers and users.
  • Propose how we could use the data to improve our systems for the benefit of advertisers, users, and Google.
  • Implement and evaluate prototypes all the way from data ingestion, through adding signals to our retrieval model and training it to running live experiments and evaluating their impact.
  • Participate in productionizing the system by automating it and making it debuggable and monitored.

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