Staff Software Engineer, Search Ads Auction Mechanism
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Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 8 years of experience programming in Java or C++.
- 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 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.
- Experience in experiment design.
- Experience with data structures and algorithms in either an academic or industry setting.
- Experience with auction theory.
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.
With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.
Search Ads is a business with a global economic impact-a core component of this economic engine is the "ad auction" which makes some of the most critical decisions related to value-creation — for each search query, auctions decide which ads are eligible, how to rank and allocate them on the results page, and what to charge. Our team designs new algorithms that make these economic decisions with the goal of optimizing user engagement, advertiser Return on Investment (ROI), and Google business. We operate as a growth team and use the scientific approach of iterating through hypothesis, experimentation, and analysis loops to drive growth.
Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google. We’re made up of multiple teams, building Google’s Advertising products including search, display, shopping, travel and video advertising, as well as analytics. Our teams create trusted experiences between people and businesses with useful ads. We help grow businesses of all sizes from small businesses, to large brands, to YouTube creators, with effective advertiser tools that deliver measurable results. We also enable Google to engage with customers at scale. Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $207000 - $301000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Write analyses to test hypotheses about the user-value landscape and what improvements (e.g., new mechanisms or improvements to existing ones) might be appropriate.
- Implement (including testing) such mechanisms in the auction.
- Design experiments to evaluate our hypotheses, and tease out small-but-crucial effects in improving user/advertiser value; then analyzing the results.
- Document the findings of analyses, and the design of mechanisms (at the conceptual level and the serving-implementation level).
- Our mode of operation is a mix of research scientist, data scientist and software engineer. We are constantly making hypotheses about the way the world works, testing them experimentally, working out what auction changes are appropriate, testing those, and iterating to get to a more ideal state.
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