How to Become a Machine Learning Engineer – A Complete Career Guide

In This Guide:

5 steps to become a machine learning engineer
What is a machine learning engineer?
What does a machine learning engineer do?
Machine learning engineer job description
Machine learning engineer salary
Machine learning engineer job outlook

We all know the basic premise of artificial intelligence. AI is how machines are able to learn from their environment and increase intelligence over time. Most people were introduced to the whole idea through Hollywood cinema, which has given us a bleak look at the scary potential of what could happen with AI in the wrong hands, but many watching 2001: A Space Odyssey decades ago were not aware that that artificial intelligence has been around, at least in simplified form, since the 1950s. AI is a broad field encompassing many different applications, and it is now being employed on a large scale in a variety of organizations. As defined today, machine learning is one subset of AI that works with big data applications and is accomplished through advanced mathematics and software programming.

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What is a Machine Learning Engineer?

Machine learning is a form of AI that enables a system to learn from data rather than through explicit programming. Once an ML program is written, it must be “trained” before it is deployed in its intended use. Training is the process by which the machine learns. The programming utilizes algorithms that ingest training data supplied by a machine learning engineer, making it possible to produce more precise models based on that data. A machine learning model is the output generated after a machine learning algorithm is trained with data ingestion. Once trained, when a machine learning model is fed real-world data, it produces an output. A predictive algorithm will create a predictive model. When the predictive model is provided with data, it puts out a prediction based on the data that trained the model.

Through its training and iterative online learning, a machine learning model can vastly improve its understanding of the types of associations that exist between data elements. Due to their complexity and size, these patterns and associations would be easily overlooked by human observation. Machine learning techniques are required to improve the accuracy of predictive models. Depending on the nature of the business problem being addressed, there are different approaches based on the type and volume of the data.

A machine learning engineer must understand each of these approaches, as well as how and in what situations to apply them. The four basic approaches utilized are supervised learning, unsupervised learning, reinforcement learning, and deep learning. The differences between these approaches lies in the data to be used to create a learning model. Supervised learning is used with data already supplied with labels, for example, identifying animals from new images. Unsupervised learning is used with unlabeled data and is often used to identify spam or junk emails using unidentified elements within the email. Reinforcement learning is similar to supervised learning in that it uses labeled data. However, reinforcement learning is done without the benefit of training data, instead improving its modeling via trial and error from real-world data. Deep learning incorporates neural networks to learn from data in an iterative manner. It is especially useful in learning patterns from unstructured data in applications such as speech and facial recognition.

A machine learning engineer must have an advanced knowledge of mathematics to recognize different types of data sets and be able to define at least rudimentary patterns and tendencies in the data. Utilizing a machine learning platform, such as IBM, Microsoft, Google and Amazon, an ML engineer must then utilize advanced programming techniques and algorithms to create a system capable of ingesting a particular type of data and turning it into the desired modeling output.

What does a Machine Learning Engineer do?

With advanced skills in mathematics, programming and data science, machine learning engineers evaluate data streams and determine how best to go about producing models that return polished information to meet an organization’s needs. Once the programs are written, ML engineers provide data to help the system learn how to interpret data and make predictions or draw conclusions. When the system has been sufficiently trained, it goes live in whatever setting is needed. Machine learning engineers then must monitor the system’s performance and evaluate the data being returned by the modeling, to ensure its accuracy. In smaller organizations, machine learning engineers often double as data scientists, but in larger organizations the two professionals work collaboratively to provide clean data and create an optimal machine learning system that data scientists will then utilize to deliver required data.

The tools of the trade are the machine learning platforms, which are then utilized as the foundation for complex programs that ingest data and learn how to make the most accurate identifications, predictions, or whatever other modeled output is required. Programming languages most commonly used include, but are by no means limited to the following:

Python
Java
C
C++

JavaScript
R
Scala
Julia

Machine learning engineers must also have strong familiarity with the standard algorithms utilized for programming and modeling. Customized algorithms are sometimes required, or just alterations to the standard algorithms, but knowledge of these algorithms across the four basic approaches (supervised learning, unsupervised learning, reinforcement learning, and deep learning) is critical. Some of the most widely used algorithms include the following:

Decision trees
Naïve Bays Classifications
Ordinary Least Squares Regression
Logistic Regression
Support Vector Machines

Ensemble methods
Clustering algorithms
Principal Component Analysis
Singular Value Decomposition
Independent Component Analysis

ML engineers must also record their processes and results and report findings to their own organizations, and sometimes outside stakeholders.

Five Steps to Become a Machine Learning Engineer

Step 1: Undergraduate Degree

As the primary knowledge requirements for a machine learning engineer are mathematics, data science, computer science and computer programming, an undergraduate degree for an aspiring machine learning engineer should ideally be in one of those disciplines. Alternate degrees in related fields, such as statistics or physics, can also be applicable. Machine learning engineers must also have a strong business acumen to understand the data needs of employers, so degrees in business can also be a good starting point, but it must then be supplemented with extensive technical training in the necessary sciences.

Step 2: Initial Career Options

A machine learning engineer is not an entry-level position, but where does anyone start who may have the goal of becoming a machine learning engineer? Here are a few possibilities.

Software Engineer
Software Programmer
Software Developer

Data Scientist
Computer Engineer

Step 3: Earn a Master’s Degree and/or Ph.D.

An undergraduate degree alone will not be enough for the vast majority of machine learning engineer job openings. Master’s degrees in data science, computer science, software engineering or the like, and even a Ph.D. in machine learning would provide a great many options for machine learning engineers.

Step 4: Post-graduate Career Path

Additional education and experience will enable professionals to at least get their foot in the machine learning engineer door but will also provide other options. Management leadership positions will become available to those with experience and education, and of course strong leadership skills. There is also a good deal of research into AI and machine learning being conducted, largely by mega tech companies like Apple, Google and Microsoft. These research positions may very well determine the future of machine learning. Some organizations that can’t justify a full-time machine learning staff hire freelance machine learning engineers to build and implement particular ML systems, so freelancing can be a lucrative and flexible professional career path. And for those with a desire to teach the next generation of machine learning engineers, university faculty posts will of course be well within reach.

Step 5: Never Stop Learning

In any technical industry, particularly one advancing so rapidly and dramatically as machine learning, keeping up with the times is critical. Always be aware of and learn new algorithms, machine learning platforms, programming languages, machine learning libraries, etc. Take continuing education courses, obtain professional certifications, and develop a network of other machine learning engineer professionals.

Machine Learning Engineer Job Description

While the basic duties of a machine learning engineer may be largely similar from organization to organization, the details will vary substantially. This will depend on the nature of the organization, what its primary needs and goals are for machine learning, and the experience level of machine learning engineer sought. Details typically included in a machine learning engineer employee wish list include some of the following:

Work with Data Scientists and Business Analysts to frame problems in a business context
Build data pipelines that pull data from various sources
Build and maintain learning models and machine learning infrastructure
Select appropriate datasets and data representation methods
Design experiments and analysis methodologies that are statistically rigorous

Run machine learning tests and experiments
Perform statistical analysis and fine-tuning using test results
Build a user interface to interact with machine learning models through simulations, visualize model metrics and collect domain expert feedback
Participate in code reviews to ensure code quality and share best practices and experiences with the team

Machine Learning Engineer experience and skills

Machine learning engineers are generally expected to have at least a master’s degree, and sometimes a Ph.D. in computer science or related fields. Advanced knowledge of mathematics and data analytical skills are critical components of a machine learning engineer’s background. Because processes and results must be communicated to management and/or outside stakeholders, ML engineers must also have strong written and oral communication skills. Specific experience and knowledge typically required by hiring employers may also include the following:

Experience with machine learning platforms such as Microsoft Azure, Google Cloud, IBM Watson, and Amazon
Knowledge of machine learning programming languages such as Python, C, C++, Java, JavaScript, R, and Scala
Knowledge and expertise with probability and statistics
Knowledge and expertise with data modeling and evaluation

Experience with the application of machine learning algorithms and libraries
Strong software engineering and development skills

Machine Learning Engineer Salary

The average annual salary of machine learning engineers is $112,806, plus bonuses and profit sharing, according to Payscale. Experience has a tremendous impact on the ML engineer’s earning power. On average, entry-level positions pay about $93,000 a year, while machine learning engineers with five to nine years of experience are paid about $138,000 per annum on average. And earning power doesn’t stop growing there. Machine learning engineers with 20-plus years under their belts make on average of $162,000 a year.

Machine Learning Engineer Job Outlook

The outlook for the field of machine learning is extremely high in today’s data science landscape. In fact, Forbes recently listed it as one of the Top 10 Tech Job Skills Predicted to Grow the Fastest in 2021. Forbes determined that demand for AI and machine learning skills will grow 71% compound through 2025 and estimated that there are almost 200,000 open positions today requiring a background in machine learning. Top industries machine learning engineers include manufacturing, information technology, finance and insurance, marketing and advertising for businesses, and professional services.

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