LinkedIn Scholars is a program that enables academics to contribute directly to LinkedIn’s vision of creating economic opportunity for every member of the global workforce. Scholars will gain practical experience combining industry knowledge with academic expertise to solve complex business problems spanning all areas of engineering, with an initial focus on Artificial Intelligence and Data Science.

As a LinkedIn Scholar, you will:

  • Work alongside LinkedIn scientists and engineers on a wide range of challenges with the opportunity to create a positive impact at a massive scale.

  • Engage in our culture and network with others focused on responsible innovation. 

  • Have the ability to partner with LinkedIn to publish reports, papers, etc.

The hallmarks of this program are its emphasis on collaboration and flexibility. Scholars will work directly with a LinkedIn leader as their manager, and will jointly set key goals to work toward. Working alongside other team members, Scholars will be integrated within our engineering organization, have opportunities for mentorship, and the ability to provide advice on technological strategy. We’ll also have community opportunities for Scholars to network with each other.

Scholars have the flexibility to work in our offices or remotely and dedicate a part of their time, up to a full sabbatical period, to their work with LinkedIn. The ability to stay fully connected to academic work is an important part of the design of this program, because it allows Scholars to stay immersed in the latest theoretical developments of their fields, while also sharing real-world application learnings from their time at LinkedIn with students. Our hope is that this creates a more rich learning and research experience for academics and students alike.

We are currently looking for talented faculty from academia who would like to apply and expand their research into one of our many Data Science and AI projects. Project assignments are tailored to the needs of the project hosts and the Scholars' interests and areas of expertise.

Basic Qualifications:

  • Have a PhD/doctorate in your area of research

  • Have a track record of research and/or publications to help solve LinkedIn’s important technical problems in areas such as large recommender systems, more efficient deep learning algorithms, distributed systems, graph databases, high performance compute, etc. 

  • Have experience leading and/or working with research teams

  • Current affiliation with an academic or research institution 

Prefered qualifications include experience with:

  • Experimentation and Causal Inference to drive business decisions

  • Using AI/ML in optimizing network behavior and large-scale recommendations

  • Macro/microeconomic analysis of LinkedIn data, including network and graph theory

  • ML with heterogeneous and sequential data

Interested in being one of our first LinkedIn Scholars? Apply here with your CV/resume and one-page research interest (include the objective, background, and description of the technical problem) combined into a single PDF file. To learn more about the innovation happening in LinkedIn Engineering, visit the Engineering Blog.