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.
We are looking for Artificial Intelligence Engineering interns to work on our massive semi-structured text, graph and user activity data sets. This internship will focus on building scalable and intuitive Recommender Systems to utilize on various LinkedIn products. As an intern, you will work on creating next-generation recommendation algorithms that power LinkedIn’s products, such as the feed, jobs, and learning platforms.
Our Recommender Systems teams create personalization that enhances the user experience by showing relevant posts, videos, and connections, keeping users engaged. We are looking for interns to help ensure that users are presented with fresh and relevant content, encouraging them to keep coming back.
Candidates must be currently enrolled in a PhD 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
Responsibilities:
• Conduct research and development on cutting-edge recommender systems, applying
techniques such as collaborative filtering, matrix factorization, deep learning, and
reinforcement learning.
• Design and implement scalable algorithms to personalize LinkedIn’s platform, optimizing for
relevance, diversity, and fairness in recommendations.
• Collaborate with engineering and product teams to integrate your solutions into LinkedIn’s
ecosystem, impacting millions of users globally.
• Leverage large-scale datasets to train and evaluate recommender models, iterating on
improvements to ensure optimal performance.
• Work in a highly collaborative environment with mentors, business experts and technologists
to conduct independent research and help deliver intuitive solutions to our products and
services.
Basic Qualifications:
• Currently pursuing a PhD in computer science, statistics, mathematics, electrical engineering,
machine learning, or related technical field and returning to the program after the completion
of the internship
• Background in recommender systems, machine learning, or related areas.
• Experience with programming languages such as Python and machine learning libraries like
TensorFlow or PyTorch.
• Knowledge of key recommender system techniques, including collaborative filtering, content
based recommendations, hybrid models, and deep learning approaches.
• Experience with evaluation metrics for recommendation quality (e.g., precision, recall, AUC,
diversity).
Preferred Qualifications:
• Solid understanding of common programming languages used in AI, such as Python, Java,
C++, and R
• Experience or desire to learn Hadoop, Pig, or other MapReduce paradigms
• Hands-on experience deploying recommender systems in production.
• Expertise in reinforcement learning applied to recommendation tasks.
• Strong publication record in relevant conferences (e.g., RecSys, NeurIPS, KDD, SIGIR).
• Proficient in Python, and experience with machine learning and deep learning toolkits (e.g.,
TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)
• Excellent communication skills
Suggested Skills:
• Experience or research in Machine Learning and Deep Learning
• Experience working with large data sets and data mining
• Strategic thinking and problem-solving capabilities
LinkedIn is committed to fair and equitable compensation practices.
The pay range for this role is $57 - $70 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.
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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.
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As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link: https://lnkd.in/paytransparency.
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This document provides transparency around the way in which LinkedIn handles personal data of employees and job applicants: https://lnkd.in/GlobalDataPrivacyNotice
