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

LinkedIn is the world’s largest professional network, built to create economic opportunity for every member of the global workforce. This internship role involves working with massive data to design and build scalable recommender systems that enhance user engagement on LinkedIn's platform.

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

Skills

• 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

Benefits

• Annual performance bonus
• Stock
• Benefits
• Other applicable incentive compensation plans

Company Overview

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