Breaking into the field of machine learning may start by securing a valuable machine learning internship. Machine learning is at the core of the technology we use every day, from search engines to streaming platforms, and the demand for skilled professionals is greater than ever.
Internships are crucial for gaining practical experience, learning how to apply theoretical knowledge to real-world problems, and building industry connections. Find out what you need to know about machine learning internships, including benefits, duties, and skills like data science you’ll use on the job. Let’s get started.
Machine learning internship overview
Machine learning internships can help you lay the foundation for future professional success by providing valuable hands-on work experience. Here’s an overview of the duties and skills you can expect in the job.
What does a machine learning intern do?
The duties and responsibilities you have as a machine learning intern vary by internship, but common tasks include debugging code, working with algorithms, data analysis, and predictions. Below are some common tasks you can perform.
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Design and implement machine learning models in cloud-based environments
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Adapt and develop ML algorithms for business purposes
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Help development or engineering teams complete projects
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Help programmers test and debug their code
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Identify patterns and trends in your data
How much does a machine learning intern make?
According to Glassdoor, the median annual salary for machine learning interns is $87,919. [1]. However, the exact salary you can expect from a machine learning internship will vary widely from internship to internship, so take this number with a grain of salt. Additionally, while some internships may last for a year, many internships only last for a short period of time, usually during summer or winter break.
Skills for joining a machine learning internship
Whether you’re an aspiring data scientist or an AI engineer hoping to work in computer vision one day, you need specific skills to land a machine learning internship. These skills include functional knowledge of programming languages, the ability to use data visualization tools, and the ability to work with a team.
Here are just a few of the technical and interpersonal skills you may need to use during your internship.
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Knowledge of programming languages such as R, Python, Java, and C/C++.
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Experience building models using deep learning frameworks such as TensorFlow
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Knowledge of relevant statistical, mathematical, and computational concepts.
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Ability to collaborate with others and work in a team.
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Excellent written and oral communication skills
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A curious personality who can think critically and outside the box.
Benefits of a machine learning engineer internship
In addition to being able to add relevant job titles to your resume, you’ll gain experience collaborating with real machine learning engineers to identify problems and develop solutions to them. Additionally, you will create a portfolio that showcases your skills and accomplishments.
Internships can supplement the training you receive in school with real-world work experience and give you an idea of what your professional future could be like.
How to find a machine learning internship
There are two main ways to find internships online. You can search for internships on dedicated job sites such as Indeed and LinkedIn, or search for internships on companies’ dedicated job pages. Here’s how to use one or both of these methods.
Search for job sites
To find machine learning internships on job sites, simply visit your site of choice and search for “machine learning internships.” From there, you can view a list of currently available internships.
Popular websites for searching for internships include:
Explore a company’s career page
To find internships on a company’s dedicated job page, simply go to a search engine like Google and search for “.[business’s name] work. Once you visit our dedicated jobs page, search for “machine learning internship” and see if there are any openings. Also consider searching for related job titles, such as “machine learning engineer internship” or “data science internship.”
Machine learning internship requirements
Machine learning internship requirements vary, but typically require an understanding of computer science, knowledge of statistics and algorithms, data modeling skills, and the ability to engineer software.
You may need to enroll in a master’s or doctoral program, while others may only require enrollment in a bachelor’s program with an emphasis in computer science, engineering, data science, or a related subject.
Please note that all internships may expect you to have some understanding of programming languages such as Python or R, be able to work with ML algorithms and train ML models, and be comfortable working with different types of data.
How to apply for a machine learning internship: Application tips
When applying for an internship, it’s important to always put your best foot forward. To achieve this, there are some important points to keep in mind throughout the process.
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Please apply in advance: Be sure to apply for internships in advance, as many internships begin recruiting candidates well in advance of the start date. For example, many summer internships begin looking for applicants as early as spring or early winter.
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Leverage your personal network to find opportunities. One of the most valuable resources for finding the right internship is your existing network of colleagues, mentors, and professionals.
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Request letters of recommendation early: Some internships may require you to provide letters of recommendation with your application, so it’s important to get them early enough in the process to meet application deadlines.
Prepare for a machine learning research internship with Coursera
An internship can be a stepping stone to a future career as a machine learning professional. Prepare for your next internship with Coursera’s cost-effective online courses.
Andrew Ng’s Machine Learning Specialization from Stanford University and DeepLearning.AI provides a broad introduction to the latest in machine learning, including supervised learning, unsupervised learning, and best practices used in Silicon Valley to innovate in artificial intelligence and machine learning.
