LinkedIn Internship: Shape Your Future as a Machine Learning Engineer Intern

LinkedIn is offering internships for freshmen pursuing B.S/B.E/B.Tech degrees in computer science, statistics, mathematics, electrical engineering, machine learning, or related technical fields. This is a unique opportunity for aspiring talents. Below, you’ll find detailed information about eligibility, responsibilities, and the application process.

LinkedIn Internship 2023 Details:

Company Website:

Job Role: Artificial Intelligence – Machine Learning Engineer Intern

Qualification: BS/B.E/B.Tech

Experience: Fresher

Batch: 2022, 2023, 2024, 2025

Salary: ₹15 LPA (Expected)

Job Location: Bengaluru

Last Date to Apply: ASAP

About the Job:

LinkedIn’s mission is to help professionals achieve more in their careers. Every day, millions of people use LinkedIn’s products to make connections, discover opportunities, and gain insights. LinkedIn goes beyond being a digital resume—it transforms lives through innovative products and technology. As a Machine Learning Engineer Intern, you’ll have the chance to impact the world’s workforce and work with some of the brightest technical talent.

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Please note that this role requires full-time, in-office presence due to the nature of the work and equipment used.


  • Work with BIG data, processing millions of samples for statistical modeling, data mining, recommendation, or search relevance solutions.
  • Write production-quality code and contribute to the next generation of LinkedIn’s systems.
  • This is a full-time internship role, taking place between May and September 2023.

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Basic Qualification:

  • Currently pursuing a B.S/B.E/B.Tech in computer science, statistics, mathematics, electrical engineering, machine learning, or a related technical field.
  • Experience with one or more general-purpose programming languages: Python, Java, C/C++.

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Preferred Qualifications:

  • Experience contributing to research communities or efforts, including publishing papers in major conferences or journals and involvement in open-source projects.
  • Hands-on experience in areas such as Natural Language Processing, Computer Vision, Information Retrieval, Recommender Systems, Machine Learning, Deep Learning, Advanced Statistics, and Optimization.

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Suggested Skills:

  • Ability to design and execute research agendas.
  • Experience in Deep Learning frameworks (Tensorflow, etc.).
  • Experience with Spark, Scala, or other MapReduce paradigms.

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How to Apply for LinkedIn Software Engineering Internship 2023?

If you’re eager to embark on a Machine Learning Engineering Internship at LinkedIn, follow these steps to apply:

Apply Link: Click here

  1. Visit the official LinkedIn Careers application page through the following link: Apply Link.
  2. Explore the available positions and select “Artificial Intelligence – Machine Learning Engineer Intern.”
  3. Click “Apply Now.”
  4. Complete the online application form with the required details.
  5. Submit your application.

Frequently Asked Questions (FAQs):

1. Who is eligible to apply for the LinkedIn Internship in 2023?

Freshmen pursuing B.S/B.E/B.Tech degrees in computer science, statistics, mathematics, electrical engineering, machine learning, or related technical fields from the batches 2022, 2023, 2024, and 2025 are eligible to apply.

2. What is the expected salary for the Machine Learning Engineer Intern role at LinkedIn?

The expected salary is ₹15 LPA.

3. Is the internship location specific?

Yes, the internship is located in Bengaluru.

4. What are the basic qualifications required?

Candidates should be currently pursuing a B.S/B.E/B.Tech in related technical fields and have experience with general-purpose programming languages.

5. What are the preferred qualifications and suggested skills for this internship?

Preferred qualifications include research contributions and hands-on experience in various areas of machine learning. Suggested skills include the ability to design and execute research agendas, proficiency in deep learning frameworks, and experience with Spark, Scala, or similar technologies.



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