Data Science and Machine Learning (DS / ML) Engineers at LinkedIn develop cutting-edge machine learning models impacting millions of members. The DS – ML track may be a good fit for you have strong quantitative, analytical and problem-solving skills, regardless of the areas in which you developed or to which you apply 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 machine learning or data science

  • 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

This DS/ML apprenticeship is designed for candidates who have not recently completed a degree in computer science, machine learning, or data science. We strongly encourage applications in the following categories (but not limited to): candidates whose machine learning or data science skills are self-taught, those who are re-entering the workforce after a career pause, career switchers, those with a background in statistics or applied math in a different field, or those who have attended a boot camp-style course focusing on machine learning or data science.

Explore Data Science and Machine Learning Further!

REACH is a multi-year program where individuals with passion for engineering will work in technical roles to build their skill set and gain the experience needed to become an Engineer at LinkedIn. The time each apprentice spends in this program will vary depending on their incoming skill set, experience, and progress through the program. 
 
As a DS/ML Apprentice Engineer, you will be placed on an engineering team at LinkedIn developing cutting-edge machine learning models impacting millions of members, learning from fellow engineers and managers, and fostering key skills applicable to a career in Data Science or Machine Learning. Furthermore, apprentices are guaranteed a percentage of time to focus on their personal technical development, using both LinkedIn’s internal ecosystem and external educational opportunities. 
 
Team placements will be determined based on openings and individual technical skill sets. Roles will be based in Sunnyvale, CA. 
 
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 machine learning and data science 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 to help drive decisions (e.g. What supplies to order in a logistics setting, where to place new stores as an analyst, or working on marketing analytics project)

  • Demonstrated history of Data Science or Machine Learning-related 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 what some of these are 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 Data Science - Machine Learning and a strong passion for the subject

  • Strong collaborative skills

  • 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 Data Science and/or Machine Learning

Disclaimer: We invite you to apply to multiple roles, however, you can move forward with at most one role. We, therefore, advise you to only apply to roles you would want to be hired into. 

 

Application Requirements: 

Our application process is designed to give individuals the opportunity to show us a range of qualities we believe will make them successful in this apprenticeship. 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 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 Data Science / 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.

 

2. Your Journey into Data Science / Machine Learning

Please answer all parts listed below. We recommend your complete answer to this question be between 500 and 900 words.

 

a.    We recognize that there are many paths to Data Science and Machine Learning and we’d like you to walk us through yours. Tell us what sparked your interest in the subject and why you decided to explore that interest.

 

b.    How have you grown your skills independently or formally (consider personal projects, volunteer work, bootcamps, courses, or professional roles)? Tell us about the last technical topic you learned or are learning right now, and what your approach was. Beyond applying to this program, share how you plan to continue mastering machine learning and programming and how it fits into your long-term goals.

 

c.       What appeals to you specifically about the Data Science / Machine Learning Apprentice Engineer role over the other REACH apprenticeship roles? We recommend you reference the position guidance to understand the role you are applying to. We are looking to understand how this interest has been demonstrated. Consider projects you have worked on, courses you have taken, prior positions you have enjoyed, etc.

 

3. Your Engineering Talent

Please answer all parts listed below. We recommend that your complete answer to this question be at least 400 words, but please use as many words as you need so that we fully understand your coding examples. In your answer, we would ideally like to see direct links to code you have written that you are most proud of, and that demonstrate your awesome ability. For example, you can send us a link to your Github project, but even better would be a link to files inside the Github project that shows off your skills! We also love demo sites, videos, and outside of the box thinking! Do not use formatting or hyperlinks. Include the full URL link to any media (YouTube, GitHub, Vimeo, etc.). 

 

If you cannot share a link to the code that you’ve written, please make sure to describe the project in detail and give us as much information as you can about the project. We will need to understand your project without seeing the code, so please help us to do that.

 
At LinkedIn, every Data Scientist and Machine Learning engineer is responsible for both modeling and engineering work. Part of our evaluation process for this program is to understand your ability in both machine learning and programming. 
 
a.       Tell us about the machine learning project you are most proud of, whether it is one you completed in the past or are currently working on. You can share links to code you’ve written, a product or website you’ve built, an open-source project you’ve contributed to, and/or your favorite problem that you’ve solved. Where possible, please include independent or solo projects where we can see your direct contributions. If your examples include group work, please describe your individual contribution to the project. 
 
b.       In addition to your examples, please tell us what your objective was, what machine learning model, statistical model or embedding you trained, what results you achieved and the challenges along the way. Highlight how this coding work demonstrates your interest in this particular role.

 

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.