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LinkedIn was built to help professionals achieve more in their careers, and everyday millions of people use our products to make connections, discover opportunities, and gain insights. Our global reach means we get to make a direct impact on the world’s workforce in ways no other company can. We’re much more than a digital resume – we transform lives through innovative products and technology.

Are you interested in Backend Development? We're building the next-generation data infrastructure, including storage, streams, media and analytics platforms. Help us scale LinkedIn infrastructure to handle massive data growth across the LinkedIn ecosystem as we experience dramatic growth in membership and products. You will utilize distributed systems and algorithms and perfect your strong systems orientation skills (multi-threading, concurrency, scalability, performance). You will understand frameworks for caching, queuing, and distributed data storage, and be excited to work on cutting edge open-source systems.

The ideal intern candidate will help scale LinkedIn’s infrastructure to handle massive growth in membership, traffic, and data as we continue to experience dramatic growth in the usage of our products with focus in one or more of the areas below:

• Data Infrastructure: Build and support large scale systems (e.g. Apache Kafka, Apache Samza, Hadoop, Pinot, Espresso, Ambry, Helix etc.) and tools that enable the generation of insights and data products on all of LinkedIn’s internal and external data via self-serve computing, reporting solutions, and interactive querying

• Search, Networks and Analytics: Build and operate the platform that powers all of search at LinkedIn—which responds to thousands of queries per second with target latencies in tens of milliseconds. The platform runs in 24/7 production environment and enables search quality engineers to rapidly innovate, experiment and improve relevance—while at the same time remaining constantly available and performant to our users

• Service: Provide the technical platform for all of LinkedIn Engineering to build services, which are the essential unit of development and deployment

• Content and Community: Deliver the systems and algorithms that generate and serve feeds of professionally relevant activities and content

Candidates must be currently enrolled in a Master’s degree program, with an expected graduation date of December 2025 or later.

At LinkedIn, we trust each other to do our best work where it works best for us and our teams. This role offers a hybrid work option, meaning you can both work from home and commute to a LinkedIn office, depending on what’s best for you and when it is important for your team to be together. Our internship roles will be based in Mountain View, CA; or other US office locations.

Our internships are 12 weeks in length and will have the option of two intern sessions:

• May 27th, 2025 - August 15th, 2025

• June 16th, 2025 - September 5th, 2025

Basic Qualifications:

• Currently pursuing a M.S. degree in Computer Science, or related technical field and returning to the program after the completion of the internship

• Programming experience in one or more of the following languages: Java, C/C++, C#, Python, or Ruby

• Knowledge of core computer science concepts such as object-oriented design, algorithm design, data structures, problem-solving, and complexity analysis

Preferred Qualifications:

• Thorough knowledge of Java

• Project or professional experience building distributed, Internet-scale systems

• Project or professional experience building and applying frameworks for one or more of the following: caching, queuing, RPC, parallelism, and/or distributed knowledge

• Thorough knowledge of multi-threading, concurrency, parallel processing and distributed computing technologies

• Experience with industry, open-source projects and/or academic research in large-data, parallel and distributed systems

Suggested Skills:

• Experience or research within large-scale infrastructure

• Experience or research with distributed systems or parallel processing

• Strategic thinking skills and ability to solve technical problems

As part of the application process for this role, after an initial qualifications review, candidates are required to successfully complete the HackerRank online code challenge. Instructions for completion of the code challenge will be sent to you if your application is selected to move forward in the process.

The pay range for this role is $53 - $65 per hour. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor.

The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans. For more information, visit https://careers.linkedin.com/benefits.

Equal Opportunity Statement

LinkedIn is committed to diversity in its workforce and is proud to be an equal opportunity employer. LinkedIn considers qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class. LinkedIn is an Affirmative Action and Equal Opportunity Employer as described in our equal opportunity statement here: https://microsoft.sharepoint.com/:b:/t/LinkedInGCI/EeE8sk7CTIdFmEp9ONzFOTEBM62TPrWLMHs4J1C_QxVTbg?e=5hfhpE. Please reference https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf and https://www.dol.gov/ofccp/regs/compliance/posters/pdf/OFCCP_EEO_Supplement_Final_JRF_QA_508c.pdf for more information.

LinkedIn is committed to offering an inclusive and accessible experience for all job seekers, including individuals with disabilities. Our goal is to foster an inclusive and accessible workplace where everyone has the opportunity to be successful.

If you need a reasonable accommodation to search for a job opening, apply for a position, or participate in the interview process, connect with us at accommodations@linkedin.com and describe the specific accommodation requested for a disability-related limitation.

Reasonable accommodations are modifications or adjustments to the application or hiring process that would enable you to fully participate in that process. Examples of reasonable accommodations include but are not limited to:

-Documents in alternate formats or read aloud to you

-Having interviews in an accessible location

-Being accompanied by a service dog

-Having a sign language interpreter present for the interview

A request for an accommodation will be responded to within three business days. However, non-disability related requests, such as following up on an application, will not receive a response.

LinkedIn will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by LinkedIn, or (c) consistent with LinkedIn's legal duty to furnish information.

Pay Transparency Policy Statement

As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link: https://lnkd.in/paytransparency.

Global Data Privacy Notice for Job Candidates

This document provides transparency around the way in which LinkedIn handles personal data of employees and job applicants: https://lnkd.in/GlobalDataPrivacyNotice