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Internships are a great way to supplement your school coursework and get your foot in the door to a new career.

An internship is basically a sneak peek inside the day-to-day work of a full-time machine learning engineer, with the added bonus of getting mentorship.

Getting an internship in a tech field is incredibly useful and can help you fill out your resume when you’re looking for your first full-time job.

Plus, the projects you build as an intern go beyond what you learn in school because you will work with real data and solve actual business problems for the company.

The Machine Learning Engineer Internship Timeline

Most machine learning engineer internships last somewhere between 3 and 6 months. The goal of the internship is to prepare you for work as a full-time machine learning engineer that means that the daily schedule of a machine learning engineer intern is very similar to that of a full-time machine learning engineer.

That being said, the general timeline of a machine learning engineer internship consists of a ramp-up period, a few small projects, and then a final project and presentation.

The Ramp Up

The first week of your internship is when you will learn the ropes. You’ll be assigned a team and probably a mentor who works full time as a machine learning engineer. This person will help you understand how the company uses machine learning in their product, the challenges they face, and get you acquainted with the tech stack.

At this point, your mentor will probably work on a project with you or assign you something simple to work on that they can review with you afterward.

If this is your first office job, you may participate in other projects to help you ramp up to get acclimated to the daily cadence of meetings and team communication.

Small Projects for Machine Learning Engineer Internships

Once you have a good understanding of how things work, you’ll begin to get more assignments to do on your own.

The projects could range from conducting research to developing data models, or normalizing data to collaborating with software engineers and product managers.

During this time, it’s important to get comfortable with the flow of working with a team and contributing on a regular basis. All of the skills you learn in these small projects will help you in the last month or so of your internship when you work on your bigger, final project.

The Final Project

As your internship comes to an end, most companies will want you to work on a final project that pulls together everything you learned, then present it to the rest of your team.

The final project usually revolves around a real business problem that the company needs resolved and you will be the one to figure it out.

You may work with other interns, or you may work alone. But, in this part of the internship, it’s important to prove that you’re ready to take on the whole machine learning pipeline.

In your final project, you will need to:

  • Understand the business problem you are assigned
  • Collect the data you need (ask for help if you need it!)
  • Determine what will make your machine learning model successful
  • Preprocess the data
  • Build a machine learning model
  • Evaluate your model based on the success criteria you defined earlier

Once your model has reached the point of success (or your internship has ended), present your findings to your team. A lot of times, the company will actually use the work you have done in production.

How to Get a Machine Learning Engineer Internship

While internships have traditionally had a bad wrap as a sneaky way for companies to get free or cheap labor, most tech companies pay their interns really well. According to ZipRecruiter, Machine Learning Interns make an average of $50 an hour, with the highest earners reaching almost $100 an hour.

Udacity’s Machine Learning Nanodegree will help you gain the skills you need to stand out from others competing for the internship. At 10 hours a week, you can finish the program in 3 months.

During the course, you’ll learn about deploying machine learning models in product and even build a real plagiarism detector. The experience from the Nanodegree program is second only to an internship.

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