Machine learning is considered one of the hottest skills in tech. Organizations everywhere are betting big that machine learning will make their apps and services “smarter,” giving them a crucial advantage over the competition. If you decide to become a machine learning engineer, you’re potentially unlocking a great number of fulfilling (and highly lucrative) opportunities.

Artificial intelligence (A.I.) and machine learning jobs have jumped by almost 75 percent over the past four years. With the global machine learning market expected to reach $209.91 billion by 2029, it’s no wonder that machine learning engineers who know their stuff can pull down extraordinary total compensation ranging from $215,000 to as much as $397,000 on an annual basis.

What is a Machine Learning Engineer?

Machine learning engineer is a hybrid role that sits at the intersection of data science and software engineering. They are primarily responsible for creating algorithms that use data to automate predictive models. Their goal is to allow computers to predict future events, apply what they’ve learned, and grow more intelligent over time.

To put it into perspective, a data scientist would analyze data to generate business insights, whereas a machine learning engineer would turn the data into a product, noted Kurtis Pykes, a self-taught machine learning engineer. Sound interesting?

Here’s a look at the core responsibilities of ML engineers, as well as the skills, qualities and experience you need for the role. While responsibilities vary by organization, team and project, some common duties include:

  • Researching, designing and implementing ML models and systems
  • Implementing machine learning algorithms and tools
  • Scaling data science prototypes
  • Selecting appropriate data sets, verifying data quality, cleaning and organizing data (in collaboration with data engineers)
  • Performing statistical analysis
  • Executing tests and optimizing machine learning models and algorithms
  • Monitoring systems in production and retraining them to improve performance
  • Utilizing machine learning libraries

Technical Skills and Knowledge Requirements

Because machine-learning engineering is not an entry-level role, most technology professionals transitioning into it have prior software engineering experience or a degree in computer science, engineering, mathematics or data science. With companies looking to build out their data science teams, new pathways to become a machine learning engineer have opened up, including self-taught options.

Whether you want to jump in by augmenting your current skillset or start from the ground up, here are the technical skills you will needto master:

Programming: Python and R are the most popular languages for machine learning practitioners; however, some companies may require you to know C++, JavaScript and Java. Here is a list of free R programming courses and a list of free and paid courses for learning Python, SQL, data science and other data analysis skills online.

Mathematics, probability and statistics: If you don’t have a CS or math degree or need a refresher, an online course is probably the best way to learn the fundamentals.

Machine learning algorithms and frameworks: It’s doubtful that you’ll have to implement a machine learning algorithm from scratch, Pykes said. However, being aware of the pros, cons and trade-offs when choosing a suitable model and optimizing it for the task requires good knowledge of machine learning algorithms, their hyperparameters and how each hyperparameter impacts learning.Because no framework is universally better than another, you’ll need to learn how to select a framework that matches your business needs by completing learning projects.

Software engineering and system design: A machine learning engineer must understand various software engineering best practices (i.e., version control, testing, documentation, modular coding, etc.) and how the different pieces form a system.If you don’t have hands-on experience, a CS degree or are coming from a data scientist/analyst role, you will need to learn the fundamentals.

MLOps: Machine learning operations (MLOps) is one of the core functions of machine learning engineering. To learn MLOps, Pykes recommends studying the lessons featured on Made with ML and reading “Introducing MLOps: How to Scale Machine Learning in the Enterprise,” and “Practical MLOps: Operationalizing Machine Learning Models.”

Soft Skills are Key

While machine learning engineering is a technical job, soft skills are essential to project success and can have a positive influence your career. Here are some of the most critical ones:

Communication and teamwork: Because machine learning engineers often work as part of a larger data science and/or cross functional team, the ability to communicate information to technical and non-technical audiences and collaborate are vital skills.

Problem solving: Naturally, one of the most important skills that any engineer needs to bring to the table is the ability to think critically and solve problems. However, transitioners note that studying machine learning is different from actually doing machine learning. To hone your skills, solve real-world problems without a fixed dataset using the entire workflow.

Time management: ML engineers often need to research, plan and execute multiple projects and meet the the needs of several stakeholders simultaneously.

Continuous learner: To stay current in this rapidly evolving field, you must have a knack for rapidly learning new tools, how they work, where they work well, and where they don’t.

Acquire Competence and Confidence

Newly minted machine learning engineers have two things working against them when it comes to landing their first job. First, despite the short supply of qualified candidates, hiring managers want to see mastery of the most important skill sets and tools before extending an offer. Second, newcomers may experience a lack of confidence or self-doubt about their abilities (commonly referred to as imposter syndrome).

What’s the solution? Acquiring hands-on experience and building a portfolio while you’re studying can not only increase your competence and your confidence but improve your chances of getting hired. Don’t wait to practice your skills, Pykes said. For instance, apply your newly acquired skills continuously through project-based learning.

There are basic machine learning projects geared toward beginners who are proficient with R or Python and projects suited for those with intermediate and advanced machine learning skills. Blogging about your projects can help you understand the nuances behind your work, sharpen your communication skills and make valuable connections with recruiters and hiring managers.

Another option is to participate in data science competitions such as DataCamp and Kaggle. Contributing to such competitions is highly regarded among many employers, and it serves as a great way to build a portfolio. You can get an idea of what it is like to participate in a competition with this Kaggle Competition Tutorial.  

While there’s a steep learning curve to become a machine learning engineer, the rewards are definitely worth it. Machine learning will only become more crucial to organizations everywhere in coming years.