How to Build a Career in AI, Part 4 How to Sequence Projects to Build a Career

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An illustration of a person thinking about his projects pillar

Dear friends,

Last week’s letter focused on coming up with AI project ideas, part of a series on how to build a career in the field. This letter describes how a sequence of projects might fit into your career path.

Over the course of a career, you’re likely to work not on a single AI project, but on a sequence of projects that grow in scope and complexity. For example:

  1. Class projects: The first few projects might be narrowly scoped homework assignments with predetermined right answers. These are often great learning experiences!
  2. Personal projects: You might go on to work on small-scale projects either alone or with friends. For instance, you might re-implement a known algorithm, apply machine learning to a hobby (such as predicting whether your favorite sports team will win), or build a small but useful system at work in your spare time (such as a machine learning-based script that helps a colleague automate some of their work). Participating in competitions such as those organized by Kaggle is also one way to gain experience.
  3. Creating value: Eventually, you gain enough skill to build projects in which others see more tangible value. This opens the door to more resources. For example, rather than developing machine learning systems in your spare time, it might become part of your job, and you might gain access to more equipment, compute time, labeling budget, or head count.
  4. Rising scope and complexity: Successes build on each other, opening the door to more technical growth, more resources, and increasingly significant project opportunities.

In light of this progression, when picking a project, keep in mind that it is only one step on a longer journey, hopefully one that has a positive impact. In addition:

  • Don’t worry about starting too small. One of my first machine learning research projects involved training a neural network to see how well it could mimic the sin(x) function. It wasn’t very useful, but was a great learning experience that enabled me to move on to bigger projects.
  • Communication is key. You need to be able to explain your thinking if you want others to see the value in your work and trust you with resources that you can invest in larger projects. To get a project started, communicating the value of what you hope to build will help bring colleagues, mentors, and managers onboard — and help them point out flaws in your reasoning. After you’ve finished, the ability to explain clearly what you accomplished will help convince others to open the door to larger projects.
  • Leadership isn’t just for managers. When you reach the point of working on larger AI projects that require teamwork, your ability to lead projects will become more important, whether or not you are in a formal position of leadership. Many of my friends have successfully pursued a technical rather than managerial career, and their ability to help steer a project by applying deep technical insights — for example, when to invest in a new technical architecture or collect more data of a certain type — allowed them to exert leadership that helped the project significantly.

Building a portfolio of projects, especially one that shows progress over time from simple to complex undertakings, will be a big help when it comes to looking for a job. That will be the subject of a future letter.

Keep learning!



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