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10 Things Google has found to be true:

1) Focus on the user and all else will follow.

2) It's best to do one thing really, really well.

3) Fast is better than slow.

4) Democracy on the web works.

5) You don't need to be at your desk to need an answer.

6) You can make money without doing evil.

7) There's always more information out there.

8) The need for information crosses all borders.

9) You can be serious without a suit.

10) Great just isn't good enough.

In a word, Google's goal is to do important stuff that matters to a lot of people. In pursuit of that goal, we've developed a set of values that drive our work, including one of our most cherished core values: "Don't be evil."






We appreciate our diverse workforce, and encourage applications from outstanding women and minority candidates.

Find out more about Google's profile, history, and corporate values in our Corporate Info section.











Learn more about Google opportunities:

- Current open positions

- Inside Google photos

- Benefit information

- Great food


Looking for interesting work that matters to millions of people?

What is it like to work at Google?

Working at Google means solving fascinating problems and making a positive difference in tens of millions of lives every day. This work has opened up interesting new areas for us and presented challenges that are not only new to us, but new to everyone in computing. These new problems require exceptional thinking and technical expertise to solve, but their solutions could dramatically improve the accessibility of information for everyone in the world. Here's a sampling of the kinds of things we work on at Google:

Large-scale computer systems problems, such as:

  • Designing and improving software that can crawl and index billions of web pages and other documents, comprising 20+ TB of raw data, in a few days.
  • Developing efficient implementations for large-scale mathematical problems, such as running Google's Pagerank™ algorithm on a graph of 3 billion nodes and 20 billion edges.
  • Developing algorithms and heuristics to keep our index up to the minute by finding and reindexing almost all web pages within minutes of when they change or they are created.
  • Efficiently and rapidly searching our full index of more than 4 billion web pages more than 200 million times per day (i.e. more than three thousand queries per second at peak traffic times), plus providing search over our archives of 20 years of Usenet data (700M+ messages) and 390M+ images.
  • Harnessing the computational resources of our more than 10,000 computers to solve large-scale problems (e.g. not just indexing documents but experimenting with new ranking algorithms, running machine learning algorithms on terabytes of data, etc.)
  • Using millions of computers to solve important problems requiring substantial CPU resources, such as cancer and disease research. For example, we have recently begun small-scale tests with the Folding at Home project at Stanford University with a few thousand selected Google Toolbar users, in preparation for a much larger scale system that would enable our millions of Google Toolbar users to opt-in to contributing their CPU cycles to solving important problems.
  • Building large-scale distributed file systems and other infrastructure that makes it possible to reliably and efficiently manage and process hundreds of terabytes of information.
  • Dealing with low-level networking issues as we crawl the web and serve user requests.
  • Developing Google's Search Appliance product, consisting of custom hardware and software for deploying Google's technology for searching private content.

Practical application of machine-learning techniques

  • We apply machine-learning techniques to learn relationships and associations within the data that we have. Our spelling correction system is a good example (spehl korector? phonitick spewling? who needs a dictniary?). People searching for Britney Spears have clearly found it useful on many occasions.
  • In the future, we'd like to improve our search quality by applying machine-learning, artificial intelligence and information retrieval techniques to problems such as:

    • Extraction of structured information from the web
    • Information synthesis (by pulling partial information from multiple documents to fulfill an information need)
    • Learning of semantic concepts and using this to improve search
    • Automatic development of vertical search services
    • Answering of natural language queries>
    • >Automatic machine-translation between language pairs
  • An unusual aspect of machine-learning work here is our ability to process very large amounts of interesting data with large numbers of computers to solve interesting problems.

Scanning and providing search over large amounts of published materials.

Automatically identifying important trends

  • Using Google's information to keep track of the important new developments, and automatically extracting summaries for people who need this information.
  • See our automatically constructed news summary page for an initial application in this area (this system automatically identifies the most important new stories of the moment and clusters together articles from different publications).

Developing Google's advertising products, so that we can maximize revenue by optimally targeting advertisements from tens of thousands of advertisers.

  • For example, part of our advertising system solves a complex system of hundreds of thousands of equations to more accurately target our ads to our available advertising inventory.

In addition, there are many other ideas and products which are not listed here because we're not yet ready to share them publicly.

Google's mission is intentionally broad and we actively explore interesting ideas, even when those ideas are risky and not assured of success. We are looking for bright, creative, and talented individuals who enjoy doing the research, engineering, product development and product management to transform ideas like these into products that are not just beneficial, but essential to millions of people every day.

Google Myths & Realities

We often hear myths and misconceptions about Google that we would like to dispel:

What we look for when hiring great people:

  • People with broad knowledge and expertise in many different areas of computer science and mathematics, including distributed systems, operating systems, data mining, information retrieval, machine learning, performance optimization, algorithms, user interface design, statistical inference and information theory, and related areas.
  • People with world-class programming skills.
  • People with excellent communication and organizational skills.
  • People who are passionate about their work and are great colleagues.
  • People who enjoy working in a high-energy, unstructured environment on very small project teams to build amazing products used by millions of people every day.
  • People with diverse interests and skills.

Life at Google

Google's culture is strong and inclusive, and we have an unusually open organization, where communication is actively encouraged among all employees and business information is broadly disseminated. If you think you'd enjoy working on problems like those described above and you have a strong record of accomplishment in a fast-paced technology environment, please send your resume and a brief cover letter to:

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