At LinkedIn, our engineering teams are constantly working to keep the company at the cutting edge of innovation to deliver value for our members and customers. We recognize that many innovations are happening within academia and partnering more closely with them will strengthen our ability to research, ideate and accelerate the value we are able to deliver to...
-
- Topics:
- artificial intelligence,
- Recommender Systems,
- nlp
-
Co-Authors: Alex Tsun, Bo Ling, Nikita Zhiltsov, Declan Boyd, Benjamin Le, Aman Grover, and Daniel Hewlett Introduction One major goal of the LinkedIn Talent Solutions team is to match job seekers and job posters, leading to mutually beneficial outcomes. A service that any LinkedIn member can use is JYMBII (Jobs You May Be Interested In), which uses information...
- Topics:
- machine learning
-
Co-authors: Ishan Shah, Alasdair James King, and Zhenggen Xu To support the consistent growth in hyperscale environments, our infrastructure relies on a plethora of storage databases and data processing nodes across its fleet. Those services reap performance benefits from NVMe’s (Non Volatile Memory Express) super-low latency. Currently, many services still use...
- Topics:
- data center,
- Storage
-
Co-authors: Adam Leon and David Golland At LinkedIn, relationships matter. On our platform, we focus on helping our members build and...
- Topics:
- Recommender Systems
-
Co-authors: Remi Mir, Tianhao Lu, Xiaoqiang Luo, Yunpeng Xu Introduction LinkedIn’s Economic Graph is rich with nodes representing...
- Topics:
- economic graph
-
MySQL is the first database choice for applications at LinkedIn that require a relational database store. Managing a large number of...
