Chapter 1 Three Steps to Career Growth

The rapid rise AI has led to a rapid rise in AI jobs, and many people are building exciting careers in this field. A career is a decades-long journey, and the path is not straightforward. Over many years, I’ve been privileged to see thousands of students, as well as engineers in companies large and small, navigate careers in AI.

Here’s a framework for charting your own course.

Three key steps of career growth are learning foundational skills, working on projects (to deepen your skills, build a portfolio, and create impact), and finding a job. These steps stack on top of each other:


    Initially, you focus on learning foundational skills. Chapters with the cover topics about learning foundational technical skills.


    After having gained foundational technical skills, you will begin working on projects. During this period, you’ll also keep learning. Chapters with the focus on projects.

  • JOB

    Later, you will work on finding a job. Throughout this process, you’ll continue to learn and work on projects. Chapters with the focus on a job search.

These phases apply in a wide range of professions, but AI involves unique elements. For example:

  • Learning foundational skills is a career-long process:

    AI is nascent, and many technologies are still evolving. While the foundations of machine learning and deep learning are maturing — and coursework is an efficient way to master them — beyond these foundations, keeping up-to-date with changing technology is more important in AI than fields that are more mature.

  • Working on projects often means collaborating with stakeholders who lack expertise in AI:

    This can make it challenging to find a suitable project, estimate the project’s timeline and return on investment, and set expectations. In addition, the highly iterative nature of AI projects leads to special challenges in project management: How can you come up with a plan for building a system when you don’t know in advance how long it will take to achieve the target accuracy? Even after the system has hit the target, further iteration may be necessary to address past-deployment drift.

  • Inconsistent opinions on AI skills and jobs rules:

    While searching for a job in AI can be similar to searching a job in other sectors, there are important differences. Many companies are still trying to figure out which AI skills they need, and how to hire people who have them. Things you’ve worked on may be significantly different than anything your interviewer has seen, and you’re more likely to have to educate potential employers about some elements of you work.

As you go through each step, you should also build a supportive community. Having friends and allies who can help you — and who you strive to help — makes the path easier. This is true whether you’re taking your first steps or you’re been on the journey for years.