Glad you asked.

We're working on lots of interesting stuff and one of the main reasons is that search is far from a solved problem.

Perhaps you're interested in Norse mythology and search for information about "Thor". The results page shows "Results 1-10" out of millions of links. That seems like a lot of information, but look at the results. Mixed in with information about the god of thunder there may be links to sites about medical equipment companies, bus builders, comic superheroes, even a movie. To someone looking for those other Thors, the Norse god is less interesting; to you, though, it's the other way around. Google prides itself on its algorithm for choosing the most relevant pages, but search has context, which is just one of the considerations in choosing the right results. Plus, the top pages listed are all in English; surely there are some interesting web pages in Norwegian (or even Old Norse) that we could translate for you, and chances are at least some of them deserve high ranking. And whether we're asking about the god, the superhero, the equipment, or the movie, there should be images, maybe movies or animations, and other rich content available; how do we find that? Plus there are many other sources of information about Thor (the god) that the web doesn't reach: ancient sagas, oral histories; who knows? So yes, Google is very good at searching the web for relevant pages for the query you type, but the web isn't enough and `relevant' is a relative term. The search problem remains far from solved.

The web really is only part of the story. There's the information the web doesn't reach and then there's personal information like mail and photos. How about the schedule of the bus you take every morning? The schedule of the bus this morning? The view from the beachfront timeshare you're thinking of renting next summer? The best route to the concert? Whether that cough is worth seeing a doctor about? Information defines the modern world; making it available and helpful is our mission.

Google is much more than search, and our mission has much greater scope.

We have our work cut out for us. Feel like helping?

You don't need to be an expert on searching. To handle information at the scale of the web requires ideas from just about every area of computer science, including information retrieval, artificial intelligence, natural language processing, distributed computing, large-scale system design, networking, security, data compression, user interface design; the list goes on. Or look at it this way: a typical query to Google can touch thousands of machines before returning the answer. With all those machines and all that communication, the problems can be daunting and new ideas and new technologies can always be brought to bear.

And then there are all the specialist domains: geospatial information (mapping), bioinformatics (health and genetics), image processing (photos, video), and on and on.

Wherever you live, there's likely to be a Google engineering office nearby. Google has engineering centers in cities throughout the US and all around the world. Each office works on problems big and small, global and local, according on the particular strengths of the engineers in the area. We want to hire the best people we can, regardless of where they live.

The Story So Far
The problems are tough, but we've made significant progress. Besides constructing the world's most heavily used web search engine and a lot of other public web services (www.google.com, news.google.com, images.google.com, groups.google.com, catalogs.google.com, labs.google.com, maps.google.com, gmail.google.com, scholar.google.com, adwords.google.com), not to mention the Google Appliance and the Google Toolbar, Google Engineering has achieved a number of other milestones you may not be as familiar with:

• We designed and built an advertising system that automatically performs hundreds of millions of automated auctions per day to determine the placement and prices of advertisements appearing on Google search result pages and non-search pages on dozens of other sites.

• We built a very large scale, distributed, fault-tolerant file system, called GFS, to help manage and process huge data sets. A paper in SOSP 2003 describes the file system.

• We designed and developed a fully automatic news system (news.google.com), which has rapidly become a standard tool for human journalists. Custom algorithms group together articles about the same story from different news organizations from around the world, providing diverse viewpoints about the day's events. Heuristics judge the importance of each story relative to other stories in the news around the world to generate our headline summary pages automatically.

• We built a searchable archive of millions of catalog pages by scanning and OCR'ing printed catalogs. This experimental service demonstrates the benefits of a searchable interface for information not previously easy to search or browse.

• We're exploring large-scale machine learning as a means of improving search quality. Our spelling correction system is one excellent example (spehl korector? phonitick spewling? who needs a dictniary?). People searching for Britney Spears have clearly found it useful on many occasions. In more recent work, we have been working on algorithms and techniques to construct very large scale Bayesian network models to help understand the relationships between words.

• We are building a large-scale public e-mail system capable of storing 2 GB of data per user, with a unique user interface that centers around search, scaling to many millions of users.

Does this sound like an environment in which you would thrive? If so, send your resume and brief cover letter to: great-engineers@google.com