How to Build a Career in AI, Part 1: Three Steps to Career Growth

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How to Build a Career in AI, Part 1: Three Steps to Career Growth

Dear friends,

The rapid rise of 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 always 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. In this and the next few letters, I’d like to share a few thoughts that might be useful in charting your own course.

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

  • Initially, you focus on gaining foundational technical skills.
  • After having gained foundational skills, you lean into project work. During this period, you’ll probably keep learning.
  • Later, you might occasionally carry out a job search. Throughout this process, you’ll probably continue to learn and work on meaningful projects.

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

  • 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.
  • Project work often means working 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 post-deployment drift.
  • While searching for a job in AI can be similar to searching for a job in other sectors, there are some 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 your work.

Throughout these steps, a supportive community is a big help. Having a group of friends and allies who can help you — and whom you strive to help — makes the path easier. This is true whether you’re taking your first steps or you’ve been on the journey for years.

I’m excited to work with all of you to grow the global AI community, and that includes helping everyone in our community develop their careers. I’ll dive more deeply into these topics in the next few weeks.

Keep learning!

Andrew

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