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.

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• 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

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