Artificial Intelligence/ Machine Learning (AI / ML) Engineers at LinkedIn develop cutting-edge machine learning models impacting millions of members. The  AI /ML track may be a good fit for you if you have strong quantitative, analytical and problem-solving skills, regardless of the areas in which you developed them or applied them. 

There are endless possibilities for the experiences that have prepared you for this apprenticeship. For example, you might be someone: 

  • Working in analytics or software engineering who has been wanting to switch to artificial intelligence/ machine learning.

  • With a background in social science who has applied quantitative methods in that arena .

  • Whose hobby is thinking about improvements to sports statistics like expected goals, home runs, or team rankings. 

Explore Artificial Intelligence/ Machine Learning Further!

Apprenticeship Details Application Questions How To Apply FAQ

As an  Apprentice Engineer, you will be placed on an Artificial Intelligence/ Machine Learning engineering team at LinkedIn developing cutting-edge machine learning models that serve millions of members. You will learn from fellow engineers and leaders, and develop key skills applicable to a career in Artificial Intelligence/ Machine Learning. Furthermore, apprentices are given a percentage of time to focus on their personal technical development, using both LinkedIn’s internal ecosystem and external educational opportunities. The time in program is a one year minimum and a five year maximum in the apprenticeship. 

At LinkedIn, we trust each other to do our best work where it works best for us and our teams. This role offers a hybrid work option, meaning you can both work from home and commute to a LinkedIn office, depending on what’s best for you and when it is important for your team to be together. 

This role is hybrid and will be based out of our Mountain View, CA office.

This role is not eligible for visa sponsorship. Applicants must be authorized to work in the US for LinkedIn without requiring visa sponsorship now or in the future. 

Job Responsibilities: 

  • Contribute a unique perspective and creative approach to solving problems at LinkedIn
  • Continue to learn and develop artificial intelligence/ machine learning skills
  • Under the mentorship and guidance of seasoned LinkedIn engineers, produce high-quality software that is tested, code reviewed, and checked in regularly for continuous integration
  • Solve difficult problems with machine learning, write code to put those solutions into production or inform business decision making, and deliver with an appropriate amount of urgency and quality
  • Develop machine learning models that will serve our 740 million members on LinkedIn.com

Basic Qualifications: 

  • An undergraduate or graduate degree (bachelor’s, master’s or doctorate) in any field
  • Demonstrated history of independent problem solving, data-driven thinking, and quantitative skills. These skills may have been gained or demonstrated as part of a degree program or prior work experience.  These do not have to be in a technology setting, but rather any situation in which you used math and code to help drive decisions or outcomes.
  • Demonstrated history of Artificial Intelligence/ Machine Learning related projects. There is a wide range of examples that will qualify, including but not limited to:
    • Open-source contributions
    • Personal analytics or machine learning projects
    • Analytical, mathematical, or statistical work as part of a job or research

 

Preferred Qualifications: 

  • Understanding of CS basic concepts: variables, recursion, algorithms, data structures, object orientation, error handling, etc. Knowing these will make it easier for you to learn more complex software topics
  • Basic knowledge of common machine learning techniques such as regression, clustering, and tree-based methods
  • Compelling desire to have a career in Artificial Intelligence/ Machine Learning and a strong passion for the subject
  • Excellent collaborative and communication skills, and ability to clearly articulate your perspective
  • Entrepreneurial mindset to bring in a new and unique perspective to the team
  • Desire to learn and develop skills in the fields of AI and Machine Learning

Our application process is designed to give individuals the opportunity to show us a range of qualities we believe will make them successful engineers. This includes their drive and ability to learn, tenacity and work ethic, unique perspective and passion for the role. As part of the application process, individuals are required to submit responses to all components of the following  questions and responses will be reviewed for completeness as well as content. 

Please note, resumes and LinkedIn profiles will NOT be considered as part of the evaluation for REACH. Therefore, please make sure that any information you would like us to know is highlighted in your application responses. While working on your application responses, prioritize authenticity. We know that tools like ChatGPT and others are widely used and can be helpful in organizing your thoughts. If you choose to use AI while writing your application, please make sure your responses reflect your own experiences, voice, and ideas. We're looking to understand your journey, interests, and baseline technical skills—and authenticity really helps us get to know you.

While LinkedIn profiles will not be reviewed in our hiring process, you must have a LinkedIn profile when applying in order for LinkedIn to receive your application through our applicant tracking system. In case you are new to LinkedIn or if you’d like some help in updating your profile, please visit this page or this video to see tips for creating a LinkedIn profile. 

1. Your Personal Story and Impact

Please answer all parts listed below. We recommend your complete answer to this question be between 400 and 700 words.

a.    At LinkedIn, we strive for a culture that embraces and represents diverse ways of thinking, background, and approaches to solving the world’s problems. Tell us how your unique experiences and background shape the point of view that you will bring to LinkedIn. We are looking to understand your unique perspective, story, and background, along with how that influences the point of view you will bring to LinkedIn and your work in Artificial Intelligence / Machine Learning.

b.    We are looking for apprentices who are committed to reaching their goal of becoming an engineer.  Tell us how you have demonstrated continuous perseverance and tenacity to achieve a long-term goal or overcome challenges and setbacks throughout your life. This could include personal and professional experiences.

 

2. Your Journey into Artificial Intelligence/ Machine Learning

We want to understand how you developed your passion for programming and machine learning, and how it aligns with your goals for the REACH program. Please answer all three of the following prompts clearly and concisely, targeting no more than 700 words for Question 2.

  1. Your Spark: What was the origin of your interest in the field? What made you decide to explore this field further?

  2. Your Growth: Highlight how you’ve grown your skills—independently or formally. This might include personal projects, volunteer work, bootcamps, courses, or professional roles. Describe how you have taken action to deepen your proficiency. Additionally, tell us about your plans for further learning and how machine learning and programming fits into your long-term career aspirations.

  3. Your Role Fit: Explain why you’re specifically interested in the Artificial Intelligence/Machine Learning Apprentice Engineer role. What about the role excites you and why is this role the right next step for you in your career? Be sure to reference the role description to align your response with the skills and interests the position requires.

What we’re looking for in your response:

  • Demonstrations of curiosity and passion for programming and machine learning. Show us where and how this passion has permeated into other aspects of your life.

  • Examples of the steps you’ve actively taken to grow your technical skills. Where have you gone above and beyond to deepen your understanding and push through challenges? Beyond bootcamps, have you tackled software engineering and machine learning projects in your personal space or sought out mentorship to achieve your goals?

  • A demonstrated alignment between your skills/experience and your interest in this role. We are looking for an understanding of the skills you have that will help you be successful and the skills you are looking to gain during your Apprenticeship.

3. Your Engineering Talent

Every Artificial Intelligence/ Machine Learning engineer at LinkedIn is responsible for both modeling and engineering work. We are looking to see a concrete example of your machine learning and programming work, and more importantly learning specific details of your process, as this will help us assess your technical foundation, problem-solving approach, and your ability to be successful in the role.**NOTE: All these criteria will potentially be covered during the on-site interview if your application is selected.**

Please answer all of the following prompts with conciseness and clear examples. Your response should be no more than 600 words.

  1. Selecting your Project: Choose a single machine learning project you’ve worked on (or are currently working on). Describe what makes the project meaningful to you, include direct links to code repositories, demos, and any other related artifacts, and give us a tour of which files to look at. If multiple people contributed, please specify which files or parts of the codebase showcase your contributions. Examples could include:
        a. A GitHub project you’re proud of.
        b. A product, publication website, or open-source initiative you’ve developed with its accompanying code.

  2. Tell us about the project
    a. Your Why and Contributions: What was the problem you were trying to solve and if it was a group project, what were your independent contributions to the project. Highlight your role in any of the following:
        i.   Data preparation
        ii.  Feature engineering
        iii. Tracking challenges
        iv. Training models/embeddings
        v.  Evaluating models/embeddings
        vi. Any other machine learning contributions.

    b. Overcoming Challenges: Explain any interesting technical choices and/or challenges you encountered in the modeling process, and how you overcame them. Did you face knowledge gaps? If so, how did you bridge them? What results did you achieve? Share how these experiences reinforced and improved your problem-solving skills and how they specifically demonstrate any relevant skills for this apprentice role. Consider touching on the following questions: If you were to do this project over again, what would you do differently? If you were to continue working on this project, what would be the next steps?

    c. Leveraging AI: AI Coding Assistants are a reality in today’s Software Engineering discipline. Share how you leveraged these tools and retained understanding of the output throughout the process. Provide a specific example of when AI helped, and when it didn’t. How did you decide what to keep, what to modify, and what to discard?

    d. Your Best Practices: Discuss any best practices you’ve learned and adopted for engineering, baselining models, and/or evaluating model performance, such as determining that a model was needed for this task and identifying the right tradeoffs to prioritize when comparing across models.

What we’re looking for in your response:

  • Evidence of technical foundations, data driven decision-making and well reasoned solutions.
  • An understanding of different aspects of machine learning development, such as data analysis, feature engineering, and model evaluation.
  • Examples of problem-solving skills demonstrated through tackling and overcoming technical challenges.
  • Technical leadership and individual contributions in group projects.
Above-and-beyond topics:
  • Advanced data processing and transformation.
  • Ensemble learning or transfer learning.
  • Custom model design (e.g., architecture, optimizer, etc.).
  • Model scalability tradeoffs.
Additional Guidance for Question 3:
  • Include plain URLs for links to code repositories, websites, or videos. Avoid clickable hyperlinks (e.g., paste GitHub URLs in plain text).
  • If you describe group projects, focus on your specific personal contributions.
  • Explicit context around decision made associated with AI/ML fundamentals (i.e. feature selection, hyperparameter tuning, etc.).

4. Undergraduate Degree

Please state any undergraduate degrees you have completed. It is a requirement for this position to have an undergraduate degree and for the degree to be noted here for us to consider the basic qualifications for this position met.

 
  1. Review the job descriptions and application questions for the positions you are interested in (posted in the role-specific pages) and draft your responses per the guidelines given. While we invite you to apply to multiple roles, you can move forward with at most one role. We, therefore, advise you to only plan to apply for the roles you would want to be hired into, and for which you are qualified.

  2. Submit your application and essay application responses before the deadline.

During the hiring process, candidates should expect the steps below:

Essay Application:

Our essay application process is designed to give individuals the opportunity to show us a range of qualities we believe will make them successful at LinkedIn. This includes their drive and ability to learn, tenacity and work ethic, unique perspective and passion for the role. As part of the essay application process, candidates are required to submit responses to all components of the four application essay questions. Responses will be reviewed for completeness as well as content. Candidates are expected to submit their responses by the application deadline.

Take-home Project: 

1 - 2 months after you applied, a recruiter will contact you if you are selected for virtual interviews following initial application review. You will be asked to complete and submit an independent take-home project prior to the virtual interviews.

Virtual Interview:

During the virtual interview, candidates will go through two interviews (one focused on technical skills and the other on soft skills) and a REACH Meet & Greet. In the technical interview, candidates will be expected to explain and extend their solution to the previously submitted take-home project. During the soft skill interview, a manager will get to know you beyond your technical skills.

Offer: 

Candidates who receive an offer will find out more details about their future team and the program. The entire application process can take between 3-4 months.

Start date: 

There will be a set hiring date so that apprentices will start in groups and go through a custom REACH onboarding experience together. Your recruiter can provide further detail on this start date during the hiring process so that there is sufficient time to prepare. 

Q: When is the next application period?   
A: We accept applications a couple times a year. The dates will be posted on this site once confirmed.

Q: How many apprentices are you accepting?  
A: We expect to hire approximately 10-15 apprentices each cycle. Exact number of hires will depend on the program’s capacity as well as business need at the time.  
  
Q: What roles are you hiring for?  
A: Our available roles vary from cohort to cohort. Generally, we have encompassed roles such as Software Engineering (Backend), Data Science, Artificial Intelligence / Machine Learning, and other roles including Technical Program Manager, and Cyber Security. To access the most up-to-date roles being offered, please refer to the "Apprenticeship Roles - April 2026 Cohort" section.

Q: Is relocation offered?  
A:  Yes, relocation is offered. Our standard relocation policies and packages apply.  
  
Q: What office location will these roles be based in?  
A: At LinkedIn, we trust each other to do our best work where it works best for us and our teams. This role offers a hybrid work option, meaning you can both work from home and commute to a LinkedIn office, depending on what’s best for you and when it is important for your team to be together. 

All roles within the cohort offer a hybrid work arrangement. The majority of our hybrid teams are based out of our Mountain View, CA office. However, we also have teams located in our San Francisco office, and on occasion, our New York office. To find the precise office location for the track you are interested in, please visit the respective track-specific page.

Q: Will LinkedIn sponsor my visa?  
A: Apprentice roles are not eligible for visa sponsorship. Applicants must be authorized to work in the US for LinkedIn without requiring visa sponsorship now or in the future.  
  
Q: How is team placement determined?  
A: The program team will determine which team an individual joins, keeping in mind the apprentice’s interests. Team assignments will be based on several factors in order to set the individual up for success.  

Q: Are all applications reviewed?
A: Based on high volume of interest, we will not always be able to review all applications. However, all applicants within this period will have an equal likelihood of review (i.e., applications will not be reviewed on a first come first serve basis).

Q: Do you need a LinkedIn profile in order to apply?
A: While LinkedIn profiles will not be reviewed in our hiring process, you must have a LinkedIn profile when applying in order for LinkedIn to receive your application through our applicant tracking system. In case you are new to LinkedIn or if you’d like some help in updating your profile, please visit this page or this video to see tips for creating a LinkedIn profile.