It’s no secret that the machine learning job market is hot. A subset of artificial intelligence, machine learning focuses on how computers can be trained to learn over time and produce more and more accurate models that predict future occurrences. Increasingly, businesses in a variety of industries like finance, healthcare, and manufacturing are hiring those with skills in machine learning to help build better products, streamline logistics, and increase their bottom line. In 2019, Indeed reported that postings for machine learning engineer jobs had accounted for 179 of every one million job postings in 2018, with the total number of machine learning engineer postings growing by nearly 350% between 2015 and 2018.
This increasing demand has translated into high salaries. When ‘machine learning (ML) engineer’ topped Indeed’s Best Jobs of 2019 list, the reported average base salary was $146,085. This growth hasn’t leveled off just yet: LinkedIn placed machine learning engineer fourth on its 2022 Jobs on the Rise list, with a salary range of $72,600-$170,000.
Indeed's best jobs of 2019
% Growth in # of Job Postings, 2015-18
Average Base Salary
Job title's # of postings per 1million total jobs, 2018
Machine Learning Engineer
As an ML engineer, you can work in exciting, breaking-edge areas like natural language processing (NLP), which focuses on teaching computers to understand and use human language, or deep learning, a kind of machine learning in which a neural network is used to optimize performance. Of course, this isn’t the only career path if you have machine learning experience — as you’ll see as you read on, there are a variety of high-demand, lucrative careers open to you, including:
Data scientist: A data scientist ideates and executes novel approaches that turn raw data into business insights and solutions using machine learning and other data analytics.
Data engineer: A data engineer focuses on building, streamlining, and maintaining the data pipelines that are central to data science, machine learning, and other data analytics.
Software engineer: A software engineer specializing in artificial intelligence or machine learning system design focuses on building and maintaining a particular product (an app, API, or service) that brings AI-capabilities to consumers and businesses alike.
What kind of education do you need for machine learning jobs?
There are multiple educational pathways to a machine learning career. The most common is to complete a bachelor’s degree in artificial intelligence / machine learning, or a bachelor’s degree in computer science with a machine learning specialization. You can also pursue an undergraduate degree in computer science with an artificial intelligence specialization. Among the AI courses you’d take for this degree would be at least one one machine learning course—and probably more.
Most bachelor’s students who take this path will graduate from their degree programs knowing how to build machine learning models. They’ll have familiarity with topics such as natural language processing, crucial for products like Amazon’s Alexa, and deep learning, essential for Tesla’s self-driving cars, fraud detection, and much more. Bachelor’s students will also gain experience with other kinds of machine learning like unsupervised, supervised, and reinforcement learning.
Oftentimes, undergraduates will complete a machine learning project as a capstone towards the end of their degree, which comes in useful as they apply for machine learning jobs. But for jobs with more responsibility and higher pay — a senior machine learning engineer or senior software engineer position, for example — companies will only interview candidates with a master’s degree in machine learning, artificial intelligence, or computer science.
While it typically won’t open the door to more senior positions right away, another way into a machine learning career is through a machine learning bootcamp: a shorter, often more intensive course of study that focuses on preparing career changers for an entry-level job. While a machine learning bootcamp can prepare you for a job in several different industries, if you’re interested in working with big data as a data scientist or data engineer, a data science bootcamp might be a better option. A data science bootcamp will generally include at least one machine learning course focused on building machine learning models, while also including training in data analysis, data engineering, data visualization, and more.
What kinds of skills do you need for machine learning jobs?
We’ve covered educational paths that will help you land a machine learning interview — but what exactly will you need to show you’ve learned in the interview? What are the skills a machine learning professional needs? What are the preferred qualifications companies are looking for when they are hiring? Different roles will naturally vary in the skills they require, and we’ll cover the details below. But every professional working in machine learning — whether as a machine learning engineer, a data scientist, or a software engineer focusing on machine learning — will require a solid foundation in mathematics (especially statistical analysis and linear algebra) and programming (e.g. Python, R, and Java). Machine learning professionals also require experience building machine learning algorithms and models and using common tools like Apache Spark, TensorFlow, and PyTorch.
Depending on the position, a machine learning professional might also need to have experience with deep learning, natural language processing, data pipelines, and machine learning system design. A machine learning engineer might need experience with computer vision, which concerns machine learning algorithms deciphering visual stimuli like in Apple’s Face ID. A data scientist, on the other hand, might need experience with business analytics and “big data,” the collective name for the massive data sets collected from our mobile phones and the other sensors, cameras, and microphones that are now ubiquitous in our daily lives.
You also shouldn’t discount the importance of soft skills: critical thinking, communication skills, and the ability to work on a team are all essential for success in machine learning and are listed as desirables in almost every job description. But what exactly do you need to be a qualified applicant for, say, a junior machine learning engineer opening? How much can you expect to earn in each of these positions? And what will your day-to-day look like? Find out below.
Machine Learning Engineer
What does a machine learning engineer do?
The primary responsibility of a machine learning engineer is to design, develop, and ship machine learning models — and then upkeep them once they’re shipped and deployed to a live product. An ML engineer might work on a team focused on building a particular product or feature, or they might work as a generalist and move between a variety of different types of projects. In addition, machine learning engineers can be responsible for managing data systems and undertaking various kinds of data analysis, such as analyzing data sets from A/B tests on a machine learning system or algorithm. All this doesn’t take place in a vacuum. ML engineers are tasked with using machine learning skills to solve real-world problems, and so their efforts can have a measurable effect on the lives of real people. Of course, things don’t always go as expected, such as when a cancer-screening machine learning model misdiagnosed individuals who had darker skin pigmentation.
What do you need to be a successful machine learning engineer?
For junior positions, most employers want applicants to have at least a bachelor’s degree or a certificate from a reputable machine learning or data science bootcamp. While the exact skills and competencies will vary depending on the role and the nature of the projects, companies generally look for junior applicants to have substantial experience writing algorithms and building machine learning models on platforms like PyTorch and TensorFlow. They also look for experience with cloud computing platforms like Azure and Amazon Web Services (AWS) and data pipeline tools like Apache Bearn. Other prerequisites are fluency in programming languages like Python and C++ and a strong foundation in statistics. For more senior positions, a master’s degree or even a PhD — and the experience and know-how that come with such degrees — will be required or strongly preferred.
In job descriptions, companies will sometimes specify that they are looking for applicants with a background in natural language processing, deep learning, or computer vision. For many jobs, especially more senior ones, companies will also want you to have some industry experience. J.P. Morgan Chase, for example, might want you to have already implemented a machine learning system for financial data, while Spotify might want you to have experience with ML-driven online recommendations.
But it’s not all about writing the perfect algorithm. Machine learning engineers need to be able to collaborate on cross-functional teams, and that means having experience communicating with people of different backgrounds and expertise. On a given day, an ML engineer might meet with a data scientist, a data engineer, and a product manager and be expected to discuss their progress on a new ML model, explain the issues with an existing ML model, or communicate how their work fits in with other business initiatives — all in a way that will be understandable to each stakeholder.
How much does a machine learning engineer make?
As mentioned above, a machine learning engineer is not only one of the fastest growing, but one of the most well-compensated jobs today. LinkedIn puts the 2022 salary range at $72,600-$170,000, and while a junior machine learning engineer should expect to earn somewhere in the lower half of this range, a senior machine learning engineer could easily earn more than $170,000 at the right company with enough experience. Compare these to the average annual salary in the US, which the U.S. Bureau of Labor Statistics estimated to be $58,260 in 2021, or the average annual salary for a web developer, which Indeed pegs at $71,707, and it becomes clear why many are flocking to artificial intelligence and machine learning programs.
What does a data scientist do?
As noted above, a data scientist ideates and executes novel approaches that turn raw data into business insights and solutions. After understanding business needs, they determine what types of data are relevant in addressing those needs and what kinds of questions need to be asked of this data, and then help develop machine learning models and other predictive analytics to efficiently carry out this analysis. After the analysis, a data scientist is usually responsible for communicating results to relevant stakeholders. In addition to these core responsibilities, data scientists may play an active role in data extraction, when unstructured data is transferred from a primary source like a social media platform to a query-able database, data cleaning, when corrupt or inaccurate data is removed from a data set, and other tasks on the data pipeline. As with machine learning engineers, data scientists may work only on one area of a company’s operations, or serve in a more generalist capacity.
What do you need to be a successful data scientist?
There is significant overlap in the skillsets of machine learning engineers and data scientists: both groups need to have a strong grasp of statistics, linear algebra, and programming languages like Python and R. Both also need facility with machine learning modeling and experience with databases, big data, and the data pipeline as a whole. Generally, however, a data scientist will work more closely with business stakeholders to not only identify business needs, but communicate actionable business insights, like what data from a social media platform might reveal about consumer sentiment and how this might impact production quotas. In addition to general business acumen, a data scientist thus needs to be excellent interpersonally, a good storyteller, and able to use tools like Tableau to create attractive data visualizations.
As data science is a slightly more interdisciplinary field than machine learning, there are more ways to gain the skills you need. While a bachelor’s or master’s in machine learning, artificial intelligence, or computer science will teach you much of what you need to know, companies also encourage those with degrees in other STEM fields to apply to be data scientists, provided they know their way around an algorithm and have the requisite experience with SQL, AWS, and other relevant technologies. If you don’t have a STEM degree or relevant experience with ML algorithms, a data science bootcamp is also a great way to get started in the field.
How much does a data scientist make?
According to the U.S. Bureau of Labor Statistics, the average salary for a data scientist in 2021 was $108,660, with those in the 90th percentile of salary earning up to $167,040. In contrast to machine learning engineering, data scientist positions are more evenly distributed throughout the country, though salaries in hotspots like California and New York still reflect the higher cost-of-living in these places.
What does a software engineer do?
Software engineers are responsible for designing, building, and shipping all sorts of software. They are also responsible for continually testing and debugging software that has already been shipped. While software engineering began vacuuming up talent during the operating system races in the 1990s and continued into the social media and eCommerce boom, increasingly software engineers are being asked to leverage experience in the relatively newer field of artificial intelligence and its subfields of machine learning, natural language processing, and computer vision. While there is lots of overlap between the responsibilities of a machine learning engineer and a software engineer specializing in machine learning, software engineers specializing in machine learning may also be pulled to code for non-ML projects, and while a machine learning engineer might work with data scientists on developing algorithms and models in tandem that might never see production, a software engineer almost always works towards a finished piece of software.
What do you need to be a successful software engineer?
Software engineers almost always come from computer science backgrounds, and many will have advanced degrees. Above all, the most important requirement is an ability to write high-quality code in a variety of languages, including C++, Java, and Python. Companies also often look for individuals with experience in software infrastructure, the basic systems that teams use to develop their own products. Frequently, companies will also reward applicants who have experience with the Scrum project management framework. Depending on the position, subject-matter expertise in machine learning or a host of other areas is necessary.
How much does a software engineer make?
According to Glassdoor, the median salary for an entry-level junior software engineer is $106,928. By the time you are a senior or principal software engineer, you can take home $133,638 annually or more. More specialized software engineers who require experience in artificial intelligence or machine learning likely earn more than those without these added qualifications.
Data analyst or data engineer
What does a data analyst or data engineer do?
Two other common occupations for people with machine learning experience are data analyst and data engineer. Data analysts analyze numerical data, develop predictive models, and communicate their findings to various business stakeholders. While data analysts might also take a leading role in designing a company’s data pipeline and other data-related processes, oftentimes these responsibilities fall under the purview of a data engineer, whose primary goal is to ensure that data analysis runs efficiently. A data engineer’s responsibilities might also involve organizing or otherwise preparing raw data and maintaining databases.
What do you need to be a successful data analyst or data engineer?
Data analysts and engineers can come from a wide variety of backgrounds, from mathematics, to computer science, to finance, to the social sciences. With a firm grounding in statistics and knowledge of Microsoft Excel, Google Sheets, and a querying language like SQL, you can already be hireable for certain entry-level data analytics jobs. Generally, data engineer positions require either a master’s degree or the equivalent in experience as a data analyst. Other important skills include proficiency with data visualization tools like Tableau and, for certain positions, experience building an ML model.
How much does a data analyst or data engineer make?
Glassdoor reports the median salary for an entry-level data analyst to be $65,664. With experience, data analysts regularly earn upwards of $100,000. Reflecting the higher requirements for education and experience, the median salary for an early-career data engineer is $114,196.
How do you get a machine learning job?
While hiring processes vary depending on where you apply, to land a job and start your machine learning career it’s crucial to have a specific machine learning resume that outlines your experience and skill-set, as well as a portfolio that shows off the projects you’ve done at school, during a bootcamp, or in your free time.
To land a machine learning interview, it also helps to have a thriving network of professionals in and around machine learning and artificial intelligence. You might start by reaching out to friends, family, and existing alumni networks. The more people you meet, the more you’ll learn about the field and the better chance you’ll have at finding someone willing to refer you for an open position. And if you don’t yet have a portfolio or a professional network, no better time to start than now—it will be worth it! While competitive to get, machine learning jobs offer substantial compensation and the opportunity to work on cutting edge technologies improving the lives of real people every day.
Of course, before putting together a resume or reaching out to alumni networks, you’ll need to apply to, get accepted by, and complete a degree program or bootcamp. To learn more about which programs might be best for you, head over to our bachelor’s, master’s, and bootcamp recommendations.