Apprentice Engineer – Artificial Intelligence​/Machine Learning

Company Description

Linked In 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.

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

At Linked In, we have developed an apprenticeship program that creates opportunity for every individual with the passion, potential, and drive to either develop or restart their career in engineering. We fundamentally believe top talent can come from anywhere, regardless of educational training or professional experience.

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  time each apprentice spends in this program will vary depending on their incoming skill set, experience, and progress through the program. The time in program is a one year minimum and a five year maximum in the apprenticeship.

This program is designed for candidates who have non-traditional career paths and we strongly encourage those candidates to apply. This includes but is not limited to: candidates without Computer Science degrees who are self-taught, those that are re-entering the workforce, individuals switching careers, or those who have attended boot camp style courses focusing on machine learning or data science.

As an Apprentice Engineer, you will be placed on an Artificial Intelligence/ Machine Learning engineering team at Linked In 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. Apprentices are guaranteed a percentage of time to focus on their personal technical development, using both Linked In’s internal ecosystem and external educational opportunities.

Is Artificial Intelligence/ Machine Learning a good fit for me? You might be a good fit for this apprentice role if you have strong quantitative 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.

You might be someone with a background in social science who has applied quantitative methods in that arena. You might be someone whose hobby is thinking about improvements to sports statistics like expected goals, home runs, or team rankings.

• 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
• Understanding of CS 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!
• Knowledge of common machine learning techniques such as…