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

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.

Were 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 youll 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.

Youll 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

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 LinkedIns platform optimizing for relevance diversity and fairness in recommendations
• Collaborate with engineering and product teams to integrate your solutions into LinkedIns 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 

Qualifications :

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
• 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) 

Preferred Qualifications:

• 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

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 $62 to $75 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 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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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.

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Pursuant to the San Francisco Fair Chance Ordinance LinkedIn will consider for employment qualified applicants with arrest and conviction records.

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No

Employment Type :

Full-time