AI/ML Engineer Intern – Search and Recommender Systems, PhD – Summer 2026 (Mountain View, CA)

LinkedIn-posted 1 day ago
$62 - $75/Yr
Intern
Hybrid • Mountain View, CA
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This internship role will be based out of Mountain View, CA. 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. We’re looking for Artificial Intelligence Engineering interns to join our team and help shape the future of how LinkedIn connects members to opportunity. As an AI PhD intern, you’ll work with massive semi-structured text, graph, and user activity data to design and build scalable, intuitive recommender systems that power core LinkedIn experiences — including the feed, jobs, and learning platforms. You’ll collaborate with world-class engineers and researchers to create next-generation recommendation algorithms that improve personalization, relevance, and engagement across millions of users. 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 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

  • 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
  • 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
  • Proven 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)
  • Proficient in modern programming languages used in AI and large-scale systems, including Python, Java, C++, and Go
  • Experience with modern data processing frameworks such as Apache Spark, Ray, Flink, or Databricks, and familiarity with distributed computing paradigms (MapReduce, cloud-native pipelines)
  • Hands-on experience building and deploying recommender systems or large-scale ML models in production (e.g., leveraging embeddings, graph neural networks, or multi-task learning)
  • Knowledge of Reinforcement Learning (RL) and Reinforcement Learning with Human Feedback (RLHF) techniques applied to recommendation or personalization tasks
  • Experience with LLM-based or hybrid retrieval and ranking systems
  • Proficiency with modern ML and deep learning frameworks — TensorFlow, PyTorch, JAX, Hugging Face Transformers, Scikit-Learn, NumPy, Pandas, etc.
  • Experience with cloud-based ML infrastructure (AWS Sagemaker, GCP Vertex AI, or Azure ML) and MLOps tools (MLflow, Kubeflow, Weights & Biases)
  • Track record of research contributions or publications in top conferences such as NeurIPS, ICML, ICLR, or KDD
  • Strong communication and collaboration skills, with the ability to translate complex technical concepts into business impact
  • Experience or research in machine learning and deep learning
  • Experience working with large data sets and data mining
  • Strategic thinking and problem-solving capabilities
  • 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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