To give you a sense of the diversity of projects engineers work on, here is a small sample:
We have some of the most valuable data in the industry at LinkedIn. To handle some of this data, we’ve built an awesome recommendation system. The recommendations engine powers many recommendations products at LinkedIn, including Jobs You May Be Interested In, Groups You May Like, and Talent Match. These products use machine learning to provide relevant recommendations, driving monetization and engagement efforts.
We’re also constantly improving questions and answers relevance to connect the recommendations engine with activity data and content tagging to provide novel rankings of question and answer content for users. All that data means we’re also working to make it more accessible using data store, search, messaging, and data services with technologies such as Krati, Rydeen, Zoie, Databus, Hadoop, Voldemort, Bobo, Kafka, and Espresso.
An ever growing number of users access LinkedIn through their mobile devices around the world. Our goal is to provide the best possible experience to the user, regardless of which device they’re using. With this mission in mind, we strive to build and enhance LinkedIn applications on platforms like iOS, Android, HTML5, and Blackberry. We’re also working on applications to power the LinkedIn experience on iPad and other tablet devices.
We’ve created LinkedIn Today to give our members the news that matters to them by learning from their networks about what is important to colleagues and the industry they’re in. Today is powered by our news relevance platform, which also powers mobile application news and industry news emails.
InVersion is the re-architecting of the LinkedIn online services. We are creating technologies and best practices to enable LinkedIn to scale 10x. We are building new infrastructure to improve resource-oriented (REST) computing, fault isolation, graceful degradation, load balancing, quality of service, parallelism, content assembly, and query processing.