What Is a Machine Learning Engineer?

Machine Learning Engineers are technically proficient programmers who research, build, and design self-running software to automate predictive models. An ML Engineer builds artificial intelligence (AI) systems that leverage huge data sets to generate and develop algorithms capable of learning and eventually making predictions.

Each time the software performs an operation, it “learns” from those results to carry out future operations more accurately.

Designing machine learning systems requires that the Machine Learning Engineer assess, analyze, and organize data, execute tests, and optimize the learning process to help develop high-performance machine learning models.

Machine Learning Engineers are highly skilled programmers who develop artificial intelligence (AI) systems that use large data sets to research, develop, and generate algorithms that can learn and make predictions.

Overall, this role is responsible for designing machine learning systems, which involves assessing and organizing data, executing tests and experiments, and generally monitoring and optimizing machine learning processes to help develop strong performing machine learning systems.

Many job descriptions call for knowledge of programming languages like Python, Java, and C/C++.

Although the specific duties will vary depending on the size of an organization and the overall data science team, a typical Machine Learning Engineer job description will include all or most of the following responsibilities:

• Designing, developing, and researching Machine Learning systems, models, and schemes
• Studying, transforming, and converting data science prototypes
• Searching and selecting appropriate data sets before performing data collection and data modeling
• Performing statistical analysis and using results to improve models
• Training and retraining ML systems and models as needed
• Identifying differences in data distribution that could affect model performance in real-world situations
• Visualizing data for deeper insights
• Analyzing the use cases of ML algorithms and ranking them by their success probability
• Understanding when your findings can be applied to business decisions
• Enriching existing ML frameworks and libraries
• Verifying data quality and/or ensuring it via data cleaning

Although you’ll find a Machine Learning Engineer can start in any number of disciplines, most ML Engineers have a background in computer science, engineering, mathematics, or data science.

A study from Indeed highlighted the differences in backgrounds for Machine Learning Engineers and other related roles, like Data Scientist, Software Engineer, Data Analyst, and Data Engineer.

Indeed’s numbers showed that the Data Scientist role clearly has the most diverse fields-of-study of these related job titles we’ve looked at, while the Software Engineer role attracted the least diverse educational backgrounds. In the case of the Machine Learning Engineer role, meanwhile, more than 60 percent of Machine Learning Engineers come from a computer science or engineering background, and they’re almost twice as likely to be from these backgrounds than someone holding the title “Data Scientist.”

As far as their professional backgrounds, the study found that the most likely prior job title for a Machine Learning Engineer would be “Software Engineer.” Many other ML Engineers are work in academia before turning to a career in machine learning.

But it’s important to remember that data science and machine learning are still in their relative infancy as fields of study and as many companies in tech and beyond are looking to build out their data science teams, new pathways to a Machine Learning Engineer are becoming possible.

Although you do need a solid foundation in math and computer science, many are picking up the other skills and knowledge areas necessary to become a Machine Learning Engineer – for example, understanding supervised and unsupervised learning, deep learning, regression, classification, clustering methods, and neural networks – by pursuing a certification course, many of which can be completed online.

Every great Machine Learning expert would seem to have a few traits in common. Here are the characteristics of a successful Machine Learning Engineer:

If you’re looking to pursue a career in AI and machine learning, you’ll need to learn to program. A programmer should understand frequently used languages including C++, Java, and Python, and it doesn’t end there. Languages like R, Lisp, and Prolog have also become important languages for machine learning. Still, not all successful machine learning engineers need to necessarily be experts in HTML or JavaScript.

You can’t master machine learning without at least a little bit of math. Whether you have a formal background in math and statistics or not, you’ll need to have at least a high-school level of math competency to keep up. At the heart of many machine learning algorithms is a formal characterization of probability and techniques derived from it. Closely related to this is the field of statistics, which provides various measures, distributions, and analysis methods that are necessary for building and validating models from observed data. Essentially, many machine learning algorithms are extensions of statistical modeling procedures.

The best ML Engineers are driven by curiosity. They don’t respond with frustration when a model or experiment fails, but instead, they’re curious to find out why.

But they also solve problems efficiently. The best machine learning pros develop generalized approaches to fixing bugs and misclassifications in their machine learning models because fixing individual bugs will be time-consuming while also making your models more difficult and complex to work with.

It’s also important to balance the determination to solve problems with the practical understanding that a lot of your models and experiments will fail. The best Machine Learning Engineers develop a sense of when it’s time to walk away.

Machine learning is by its nature an iterative process. To be effective in this role, one needs to actually enjoy that style of development. Building a machine learning system means one builds a very simple model quickly, to begin with, then iterates on getting it better with each stage.

Again, though, a good Machine Learning Engineer can’t be too stubborn. You need to develop an understanding of when it’s time to stop. It’s always possible to improve the accuracy of any machine learning system by continuing to iterate on it, but one needs to learn to develop an intuition for when it’s no longer worth the time and effort.

There is no machine learning without analyzing data. A good Machine Learning Engineer or Data Scientist needs to be able to quickly sift through large data sets, identify patterns, and know how to use that data to come to meaningful and actionable conclusions.

It’s almost like they have a sixth sense for data. Data management skills are crucial.

They should also be handy at building big data pipelines. And one needs to also understand the power of visualization. To ensure the insights you’ve unearthed are properly understood and appreciated by others, you must be handy with data visualization tools like Excel, Tableau, Power BI, Plotly and Dash.

Within the broader field of data science, there are many data professionals who perform similar roles to that of a Machine Learning Engineer. Here are a few positions that could be a part of the career path of a Machine Learning professional.

• Data Scientist: The Data Scientist role sits at the nexus of technology and business. A Data Scientist is tasked with having the business sense to understand challenges companies are facing and then using data analysis and data processing to unearth solutions and opportunities. It’s the job of a Data Scientist to find actionable insights buried in unstructured data and to use that data to perform predictive analyses. The trends and patterns Data Scientists find help companies make data-driven decisions and ultimately increase revenue. Data Scientists are also expected to be able to present their findings with eye-catching visualizations.
• Data Analyst: Data Analysts are concerned with visualization, munging, and processing data. One of a Data Analyst’s most important responsibilities or skills is optimization, where they create and modify algorithms that can be used to cull information without corrupting the data.
• Data Engineers: A Data Engineer builds and tests scalable big data ecosystems so that Data Scientists have stable and optimized data systems on which to run their algorithms. It’s also the job of a Data Engineer to update existing systems with upgraded versions of the current technologies. Data engineering also often involves building algorithms to help give companies or clients easier access to raw data.
• Artificial Intelligence (AI) Engineer: AI Engineers work with traditional machine learning techniques like natural language processing and neural networks to build models that power AI applications.
• Computer Scientist: Computer Scientists computers and computational systems. Computer Scientists deal mostly with software and software systems, including their theory, design, development, and application.
• Software Engineer: Software engineering is about using mathematical analysis and computer science principles in order to design and develop computer software. Software Engineers develop all sorts of software, including operating systems, computer games, applications, and network control systems. On a day-to-day basis, depending on the software development phase, a Software Developer will ensure active programs run smoothly, make updates, fix bugs, and create new programs. Software engineering spans a variety of technologies, from smart home devices to virtual assistants.

Depending on the size of an organization, an ML Engineer would most likely work as part of a larger data science team. That team might include Data Scientists, Data Analysts, Data Engineers, Data Architects, and Database Administrators. Beyond their own data teams, Machine Learning Engineers could conceivably collaborate with a wide variety of different stakeholders with different skills throughout an organization, including everyone from senior business leaders to marketing, sales, IT, software development, or web development teams, depending on their level of seniority.

If you’re curious about a career in data or AI, here are a few of the top reasons to become a Machine Learning Engineer.

Indeed ranked Machine Learning Engineer as the No. 1 job of 2019 for good reason: they make an average salary of $148,485 in the U.S., according to the job site (which also took into account demand and growth of postings in its ranking). Indeed’s numbers also show that one could earn up to $200,000 in one of the country’s larger markets. Machine Learning Engineers in San Francisco reported average salaries of just south of $200,000 while in New York they reported bringing home just under $170,000.

A lot of companies are taking a big interest in big data, and as a result, demand for data professionals in the job market has never been higher.

There have even been reports of bidding wars over AI talent as giants of the tech field rush to secure the top minds in the industry.

A recent report from Robert Half looking at the future of work revealed that 30 percent of surveyed U.S. managers said their company was currently using AI and ML, and 53 percent expected to adopt those tools within the next three to five years.

In other words, there’s no indication that this fertile job market is going away anytime soon.

Machine learning is a relatively new field. There are still so many solutions, tools, algorithms, and applications waiting to be created and discovered.

Similar to Software Developers, ML Engineers by nature must value learning. And using courses, blogs, tutorials, and podcasts to stay on top of a young and rapidly changing field is essential.

In fact, BrainStation’s 2020 Digital Skills Survey showed that 61 percent of data professionals take in-person courses and another 60 percent focus on workshops. Clearly, continuing education is clearly a fixture of the field.

Are you one of those individuals simply fascinated by technology, who reads with excitement about the latest advances in AI or computer applications?

In this position, you’d have the opportunity to effect real change by working on the newest and most innovative technologies. If you enjoy logic and coding, you’ll enjoy learning new programming languages for cutting-edge applications.

It’s also a great career for those who like finding practical applications for math. As a Machine Learning Engineer, you would likely be able to use linear algebra, calculus, probability, and statistics in your daily work.

If you’re the type to get bored, a Machine Learning career would feature plenty of diversity. Virtually any industry you could think of would stand to benefit from investing more money, time, and resources into mining insights from data, so you could choose to work in any industry that interests you.

You also have the opportunity to truly make a difference. You could join a team that makes the next great breakthrough in healthcare, cybersecurity, marketing, or self-driving cars. That’s an exciting prospect for many.

To succeed as a Machine Learning Engineer, you must combine the knowledge and skill sets of a Software Engineer and a Data Scientist. That means understanding all the fundamental concepts of computer science and data analysis, while also possessing some soft skills necessary to both disciplines.

A Machine Learning Engineer is expected to have many of the same competencies as a Data Scientist, including data modeling, technical proficiency with programming languages like Python and Java, and understanding how to evaluate predictive algorithms and models. An understanding of probability and statistics would be very helpful as well.

Some of the key computer science concepts important for ML Engineers are algorithms (and knowing how to write algorithms that can sort, optimize and search), understanding data structures, and having knowledge of computer architecture. Since an ML Engineer’s typical output is software, they should also understand how to follow software engineering best practices, especially those relating to system design, version control, testing, and requirements analysis.

Though a Machine Learning Engineer is often thought to sit at the intersection between data science and software engineering, there are still some competencies uniquely important to ML jobs. Many Machine Learning Engineers are now training in deep learning, neural network architectures, natural language processing, and dynamic programming.

Even though machine learning is a technical job title, soft skills are important too. Even if you possess top-notch knowledge of machine learning, you will also require polished skills in communication, time management, and teamwork. It’s also crucial that a Machine Learning Engineer is committed to lifelong learning. Given how rapidly the fields of artificial intelligence, deep learning, machine learning, and data science are changing, continuing education is necessary for any professional who wants to stay on the cutting edge.

Not only should Machine Learning Engineers possess knowledge of how to code and develop in programming languages such as Python, Java, and C++, many machine learning engineers also find it helpful to master the following machine learning tools and resources:

• TensorFlow
• Spark and Hadoop
• R Programming
• Apache Kafka
• MATLAB
• Google Cloud ML Engine
• Amazon Machine Learning