AI PhD - Reinforcement Learning - Internship Summer 25

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 interns to work on our massive semi-structured text, graph and user activity data sets. LinkedIn is seeking PhD interns to join our AI/ML team with a focus on Reinforcement Learning (RL). As a member of our team, you will develop cutting-edge RL algorithms and apply them to real-world problems, from optimizing user interactions to creating adaptive and personalized systems across LinkedIn’s platform. This internship offers a unique opportunity to push the boundaries of RL research while making an impact on millions of users 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

Responsibilities

• Conduct research and development on state-of-the-art reinforcement learning algorithms, including deep RL, multi-agent systems, and model-based RL.
• Apply RL techniques to enhance personalization, recommendation systems, and other large- scale LinkedIn products, focusing on long-term engagement and optimization.
• Develop scalable RL models that balance exploration-exploitation and adapt to changing environments.
• Collaborate with product and engineering teams to integrate RL solutions into production.
• Stay up to date with the latest advancements in RL and machine learning, contributing to leading conferences and publications.
• 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 * Research background in RL, including algorithms like Q-learning, policy gradients, actor-critic methods, or Monte Carlo methods. * Python and RL libraries (e.g., TensorFlow Agents, Ray RLlib). * Understanding of deep learning architectures applied to RL, including convolutional and recurrent neural networks

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 * Proven experience applying RL to real-world applications, particularly in recommendation systems or large-scale personalization. * Experience with multi-agent reinforcement learning or hierarchical RL. * Publication record in RL or ML conferences (e.g., NeurIPS, ICML, ICLR). * Hands-on experience deploying RL models in production. * Excellent communication skills

Suggested Skills: * Machine Learning and Deep Learning * Advanced 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.

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