AI/ML Engineer Intern – Search and Recommender Systems, PhD – Summer 2026 (Mountain View, CA)
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Mountain View, CA
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Internship
Posted March 13, 2026
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Responsibilities
Commitments
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
Commitments
This internship role will be based out of Mountain View, CA.
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.
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
This may be different in other locations due to differences in the cost of labor.
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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What still needs stronger evidence
Requirements
- Candidates must be currently enrolled in a PhD program, with an expected graduation date of December 2026 or later.
- 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 Skills
- 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
Education
- (Not required) – Candidates must be currently enrolled in a PhD program, with an expected graduation date of December 2026 or later.
- (Not required) – 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
Company Description
LinkedIn is the world’s largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. We’re also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture that’s built on trust, care, inclusion, and fun – where everyone can succeed.
Join us to transform the way the world works.
Job Description
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
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
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 https://careers.linkedin.com/benefits.
Additional 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.
LinkedIn is committed to offering an inclusive and accessible experience for all job seekers, including individuals with disabilities. Our goal is to foster an inclusive and accessible workplace where everyone has the opportunity to be successful.
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.
San Francisco Fair Chance Ordinance
Pursuant to the San Francisco Fair Chance Ordinance, LinkedIn will consider for employment qualified applicants with arrest and conviction records.
Pay Transparency Policy Statement
As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link: https://lnkd.in/paytransparency.
Global Data Privacy Notice for Job Candidates
Please follow this link to access the document that provides transparency around the way in which LinkedIn handles personal data of employees and job applicants: https://legal.linkedin.com/candidate-portal.
LinkedIn is the world’s largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. We’re also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture that’s built on trust, care, inclusion, and fun – where everyone can succeed.
Join us to transform the way the world works.
Job Description
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
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
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 https://careers.linkedin.com/benefits.
Additional 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.
LinkedIn is committed to offering an inclusive and accessible experience for all job seekers, including individuals with disabilities. Our goal is to foster an inclusive and accessible workplace where everyone has the opportunity to be successful.
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.
San Francisco Fair Chance Ordinance
Pursuant to the San Francisco Fair Chance Ordinance, LinkedIn will consider for employment qualified applicants with arrest and conviction records.
Pay Transparency Policy Statement
As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link: https://lnkd.in/paytransparency.
Global Data Privacy Notice for Job Candidates
Please follow this link to access the document that provides transparency around the way in which LinkedIn handles personal data of employees and job applicants: https://legal.linkedin.com/candidate-portal.