Even though we’re just scratching the surface of possibilities when it comes to machine learning, it’s already shaping our everyday lives and the decisions we make. Major companies like Google, Amazon, Netflix, and Tesla use machine learning to deliver personalized results to millions of users, understand and interpret human conversation, train neural networks to predict what a human driver would do, and so much more.

And there’s no sign of slowing down. The global market is expected to reach $117.19 billion by 2027 — that’s a yearly growth rate of nearly 40%.

The significant growth within machine learning, as well as the opportunities to develop new and exciting technology, has attracted many professionals to the industry. While there are the obvious titles — like Machine Learning Engineer — there are also other positions you can explore that use machine learning but might not be as obvious.

Here are seven popular jobs that use machine learning, along with information on how to get started in each role.

1. Machine Learning Engineer

Machine Learning Engineer is one of the most popular positions in the machine learning industry, and you’re likely to find many roles with this exact title during your job search. These engineers design and implement machine learning models, expand and optimize data pipelines and data delivery, and assemble large, complex data sets. Models developed by Machine Learning Engineers are used to reveal trends and predictions that can help companies meet business objectives and goals.

Machine Learning Engineers build the recommender systems that power many digital platforms. From your favorite new artist on Spotify to your next Netflix binge, many of the relevant content and products put in front of us online are thanks to recommender systems that learn our preferences. Recommender systems are powerful technologies that many of us interact with every day, and you can learn how to build them in our beginner-friendly Build a Recommender System skill path. (Or you can try our free course Learn Recommender Systems if you’ve already mastered the basics of Python and machine learning.)

On average, Machine Learning Engineers in the U.S. make $120,951 a year. Learn more about what Machine Learning Engineers do and how to land your dream job as a Machine Learning Engineer.

2. Robotics Engineer

Robotics Engineers have a huge advantage if they also have a machine learning background. Robots are often driven by either the need to emulate human behavior or to maximize the efficiency with which something can be done. So as a Robotics Engineer, you might help develop a robot’s computer vision, which would enable it to interpret and understand the visual world around it, and then make accurate — and safe — decisions. Or maybe you’d develop a machine-learning algorithm to process massive amounts of data produced by robots that assemble vehicle parts.

Designing the machines that make people’s lives easier can earn you about $99,040 a year, on average. If you’re thinking about a career in robotics, you’ll likely need to know C++ and Python, and you can get started with these in our Learn C++ and Learn Python courses.

3. Natural Language Processing (NLP) Scientist

A Natural Language Processing Scientist uses algorithms to pinpoint natural language rules, and then use them to enable computers to speak and understand the language. Machine learning makes this easier because you can design an algorithm that discovers and tests patterns for you — so you don’t have to do it manually or with elaborate spreadsheets. In a way, a Natural Language Processing Scientist builds bridges between languages and machines, making it possible for machines to understand people and vice-versa.

As an NLP Scientist, you may specialize in a subfield of NLP, such as computational linguistics, human language technologies, automatic speech recognition, or machine translation. And you’ll likely also collect, explore, and improve the quality of data used to adapt and extend machine learning-based technologies that support these areas.

U.S.-based Natural Language Processing Scientists make between $81,600 to $122,400 per year, with a median salary of $102,000. If you’re interested in a career as a Natural Language Processing Scientist, check out our How to Get Started with Natural Language Processing course or our Apply Natural Language Processing with Python skill path.

4. Software Developer

Software Developers design and build applications for mobile and desktop use, as well as the underlying operating systems. Machine learning can help Software Developers analyze data to predict how users will react to certain features of an application, design models that output data according to what users want to see, and create programs that enable chatbots to interact with end-users in more natural ways.

Generally, Software Developers fall into one of three buckets — Front-End Developer, Back-End Developer, or Full-Stack Developer — and each one focuses on a certain area of the development process.

If you’re interested in a software development position that specifically involves machine learning, you could learn TensorFlow, an open-source platform for machine learning, or Pandas, a tool in machine learning that’s used for data cleaning and analysis. Focusing on learning the tools and programming languages that are typically used in machine learning will help you qualify for these types of software development jobs.

On average, Software Developers earn around $107,510 a year.

5. Data Scientist

A Data Scientist analyzes, processes, models, and interprets data to help create actionable plans and guide business decisions for companies and organizations. As a Data Scientist, you have the potential to be one of the most useful team members in your company, largely because your ideas and suggestions are backed by hard data.

Data Scientists working in the machine learning industry help write algorithms that can discover patterns, which are then used to provide insights and recommendations. The critical role of Data Scientists is reflected in their salaries, too. You can earn an average salary of over $119,000 a year as a Data Scientist.

Learn the skills you’ll need to succeed in this role by taking our Data Scientist career path, and then once you’re ready to apply for jobs, you can check out our interview prep that’s specifically for Data Scientists.

6. Cybersecurity Analyst

Cybersecurity Analysts are in charge of figuring out the best ways to defend a company’s digital infrastructure and assets. This involves using many different technologies and can be far easier with machine learning. This is because a Cybersecurity Analyst has to collect and study large amounts of data that reflect the vulnerabilities and threats a company may face.

If you have a background in machine learning and you’re interested in working in cybersecurity, you may have the opportunity to tweak, upgrade, or create new algorithms used to protect an organization. The crucial role of Cybersecurity Analysts frequently earns them salaries in the six-figure range. The average annual pay is about $103,590.

You can learn about cybersecurity in our Introduction to Cybersecurity course, and when you’re ready to apply for jobs, be sure to check out Cybersecurity Analyst Interview Prep.

7. Artificial Intelligence (AI) Engineer

Artificial Intelligence (AI) Engineer is another position in which machine learning can be used. Since machine learning is a subset of AI, there are many AI Engineers with expertise in machine learning tools and applications.

You might develop and modify machine learning models, apply machine learning techniques for image recognition, and develop neural network applications using popular frameworks like TensorFlow and PyTorch as an AI Engineer with a machine learning specialty.

If a career in AI is in your future, skills like Python, R, and Java are common for this role, as well as linear algebra and ​​statistics. U.S.-based A.I. Engineers earn an average salary of over $164,000 a year.

What’s next?

If you’re looking for more opportunities to learn about machine learning, check out our Machine Learning Fundamentals and Feature Engineering skill paths. You may also want to learn a new programming language that’s popular in machine learning, such as Python, R, and Java.

Once you’ve picked the type of machine learning job you want, it’s important to build your resume and cover letter to emphasize the skills and experience most valuable for that position. And to prepare for the types of interview questions specific to that role. You can use this guide to help you write your technical resume, and this advice on landing a machine learning job is a great resource. Here are common machine learning interview questions that you can practice before your interviews. And be sure to check out our Career Center for more resume and interviewing tips.

Machine Learning Courses & Tutorials | Codecademy
Machine Learning is an increasingly hot field of data science dedicated to enabling computers to learn from data. From spam filtering in social networks to computer vision for self-driving cars, the potential applications of Machine Learning are vast.

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