Company Description
LinkedIn is the world’s largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. We’re also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture that’s built on trust, care, inclusion, and fun – where everyone can succeed.
Join us to transform the way the world works.
Job Description
This internship role will be based out of Headquarters in Mountain View, California.
At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team.
Are you interested in large-scale data processing? We are building the next generation of LinkedIn’s data infrastructure, spanning analytical compute, data storage, and lakehouse platforms that power insights, analytics, and intelligent products across the company. As LinkedIn continues to grow in membership, usage, and data volume, you will help scale systems that process and query massive datasets reliably and efficiently.
In this role, you will work with distributed data processing engines and algorithms, developing a strong systems mindset around scalability, performance, and correctness. You will gain hands-on experience with query execution, data partitioning, caching, and distributed storage, and contribute to production systems built on and alongside cutting-edge open-source technologies.
Candidates must be currently enrolled in a PhD program, with an expected graduation date December 2026 or later.
Our internships are 12 weeks in length and will have the option of two intern sessions
- May 26th, 2026 - August 14th, 2026
- June 15th, 2026 - September 4th, 2026
The ideal intern will contribute to scaling LinkedIn’s data infrastructure to support continued growth in membership, traffic, and data volume. As usage of our products continues to expand, this role will focus on building and supporting large-scale data systems—such as Apache Spark, Flink, Trino, Iceberg, and Airflow—that power self-serve analytics, reporting, interactive querying, and the data pipelines behind LinkedIn’s machine learning and AI-powered products.
As part of this work, the intern will:
- Design and optimize distributed data processing and query execution workflows operating at LinkedIn scale.
- Develop data abstractions and optimizations—such as materialized views and query rewriting—to improve performance, efficiency, and data freshness for analytics and AI workloads.
- Contribute to the reliability and scalability of production data pipelines through monitoring, correctness validation, and performance tuning.
Qualifications
Basic Qualifications:
- Currently pursuing a PhD 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, Scala, Python.
- Knowledge of core computer science concepts such as object-oriented design, algorithm
design, data structures, problem-solving, and complexity analysis
Preferred Qualifications:
- Experience working in a fast-paced, agile, and iterative development environment, with exposure to design reviews, code reviews, and incremental delivery of production systems.
- Strong foundation in computer science fundamentals, including data structures, algorithms, software design, and object-oriented programming, with an understanding of database management systems, query processing, and query planning and optimization.
- Familiarity with analytical query engines and optimization techniques, including materialized views, query rewriting, and cost-based optimization, or a strong interest in learning these areas.
- Exposure to open-source data and query processing technologies, such as Apache Spark, Trino, Calcite, and Iceberg, and an interest in how these systems are built, integrated, and evolved.
- Ability to reason about correctness, performance, and scalability in data-intensive systems, and to clearly communicate technical ideas in both written and verbal form.
Suggested Skills:
- Distributed Data Processing
- Database Systems and Query Processing
- Query Optimization and Execution Engines
- Large-Scale Data Management (Lakehouse Systems)
- Performance and Scalability Engineering
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.
LinkedIn is committed to fair and equitable compensation practices.
The pay range for this role is $65-$75. 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.
Additional Information
Equal Opportunity Statement
We seek candidates with a wide range of perspectives and backgrounds and we are 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 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.
San Francisco Fair Chance Ordinance
Pursuant to the San Francisco Fair Chance Ordinance, LinkedIn will consider for employment qualified applicants with arrest and conviction records.
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
Please follow this link to access the document that provides transparency around the way in which LinkedIn handles personal data of employees and job applicants: https://legal.linkedin.com/candidate-portal.
