Machine Learning Engineer Salary Guide

Machine learning engineers are in high demand. And machine learning engineer salary and benefits packages reflect that. In fact, machine learning engineering is the best job in the United States, according to an Indeed study analyzing the average salaries and job posting growth between 2015 and 2018.

So, exactly how much do machine learning engineers make? 

The average machine learning salary, according to Indeed’s research, is approximately $146,085 (an astounding 344% increase since 2015). The average machine learning engineer salary far outpaced other technology jobs on the list. The job title “full-stack developer,” for example, came in third with annual remittance of about $114,316, a 206% increase over three years.

Let’s zoom out and examine the machine learning engineer role more broadly before diving deeper into machine learning salaries.

If you’re looking for a more comprehensive insight into machine learning career options, check out our guides on how to become a data scientist and how to become a data engineer.

The Role

It’s safe to say that machine learning is the fastest-growing field in computer science today. The role can be described as an intricate mix of data science and software engineering. This is because machine learning engineers are tasked with feeding the data into data models that are defined by data scientists. 

According to Håkon Hapnes Strand, senior data science consultant at Webstep, “the role of a machine learning engineer is actually much better defined than that of a data scientist. Why? Because the companies that use that job title are the ones that have a very clear idea about how and why they want to utilize machine learning. In addition, these companies almost always have data scientists as well, so they have defined the distinction between the two. Data scientist is a job title that’s often poorly defined. It’s usually an analyst that knows some programming and machine learning. A machine learning engineer is a full-blown software engineer that has specialized in machine learning.”

Machine learning engineers are also responsible for scaling theoretical data science models to production-level models that can handle vast amounts of data in real-time. The ultimate goal here is to develop algorithms that can enable machines to analyze the information they gather, identify patterns, enable deep insights, and make decisions based on their findings. 

However, on an ordinary day, machine learning engineers will use a variety of big data tools and programming frameworks to redefine the raw data gathered from data pipelines. These are then inserted into data science models to get them ready to scale as needed.

This technology is already embedded in several products available today. If we take autocorrect, for example, it learns from previous messages and guesses the next sentence. Streaming services like Netflix also leverage smart algorithms to make recommendations based on past consumption.

However, this whole process starts with the design of complex algorithms that enable machines to gather and identify such patterns. That’s where highly sought-after machine learning engineers come in.

Market Demand

Over the past few years, the demand for machine learning engineer jobs has also surpassed the need for data scientists. According to LinkedIn’s U.S. Emerging Jobs Report, the demand for MLEs increased by nearly 10 times since 2012.

The global machine learning market is forecast to grow from $1.6 billion to almost $4 billion by 2025. That’s a compound annual growth rate of 49.7% over eight years. So you can expect the competition for machine learning engineers to grow more fierce, producing the best remuneration packages in the job market.

Where Will You Make the Most Money?

Your machine learning engineer salary will largely depend on where you’re located. Your experience and expertise are often secondary when you consider the impact of variables like the high cost of living in cities like New York City and San Francisco.

So it’s no surprise that these cities are also the highest-paying locations in the U.S. for machine learning engineers. They also have the highest demand for artificial intelligence-related careers. 

It’s important to note, however, that even if you’re making much more than your peers in, for example, cities located in the Midwest, you’ll also be spending a lot more to live in a major tech hub.

(Source: Top 10 AI Jobs, Salaries and Cities)

According to Indeed, the average salary for a machine learning engineer in Ohio is about $37.34 per hour. That’s approximately 35% below the national average. According to PayScale, machine learning professionals in Silicon Valley earn an average of 25.9% more than the national average.

Machine learning and related job titles also attract higher salaries in San Francisco (24.4% more) and New York City (8.1% more). 

With the importance of location understood, let’s explore how widely a machine learning engineer salary can range based on experience.

Machine Learning Engineer Salary: Entry-Level

An entry-level machine learning engineer typically has 0-4 years of experience. This can be someone who just got out of college or someone who just switched careers and landed their first machine learning job.

An entry-level machine learning salary ranges broadly, but the average is approximately $97,090. However, if you consider potential bonuses and profit-sharing, that number can rapidly rise to $130,000 or more. 

(Source: Average Entry-Level Machine Learning Engineer with Machine Learning Skills Salary)

Skills that can have an impact on the entry-level machine learning salary (according to PayScale) include:

• C++ Programming Language
• Big Data Analytics
• Computer Vision
• Data Analysis
• Deep Learning
• Image Processing
• Natural Language Processing
• Python
• Software Development

Machine Learning Engineer Salary: Mid-Level

Mid-level machine learning engineers typically boast 5-9 years of experience and command an average salary of $112,095. When you add potential bonuses and profit-sharing, that number can quickly rise to $160,000 or more.

(Source: Average Machine Learning Engineer Salary)

Machine learning is not restricted to technology companies. Almost everyone is trying to take advantage of this new technology, so you can expect salaries to continue to rise for the foreseeable future.

Skills that can have an impact on a mid-level machine learning salary (according to PayScale) should look pretty familiar:

• Big Data Analytics
• C++ Programming Language
• Computer Vision
• Data Analysis
• Data Modeling
• Deep Learning
• Image Processing
• Natural Language Processing
• Software Development

Machine Learning Engineer Salary: Senior Positions

Senior machine learning engineers with over a decade of experience are the industry’s unicorns. As a result, they also command the best remuneration packages in the field. If you’re a senior machine learning engineer, you can expect an average salary of $132,500.

(Source: Average Senior Machine Learning Engineer Salary)

However, because of fierce competition, bonuses, and profit-sharing, that number can quickly surpass $181,000 annually.

Skills that can have an impact on senior machine learning engineer salary (according to Paysa) include:

• C++
• Computer Vision
• Image Processing
• Matlab
• Pattern Recognition
• Python
• Signal Processing

Senior machine learning engineers are often out of reach for medium to large businesses. There aren’t many around, so it’s usually the multinationals that have the resources to hire them. 

How to Make Sure You’re at the Top of the Salary Range

As you may have noticed, starting salaries for machine learning engineers are already at the top end of tech pay. If you want to be well placed to earn even more, the first step is to pay attention to the market and pick up related machine learning engineer skills that can help boost your personal value. 

What else can you do?

Intern: There aren’t enough machine learning professionals to meet present industry demands. So if you’re still in school, you stand an excellent chance of bagging an internship long before you graduate (the same holds true if you’re already working but transitioning from another field). An internship will not only help you gain the necessary knowledge and practical, real-life experience, but also make you highly attractive to potential employers. When you apply for entry-level roles, you’ll be able to aim for the upper end of the salary range. Additionally, if you impress your employer, they may hire you for a full-time role.

Sharpen Your Skills: As you climb up the ladder, make sure that you brush up on your C++ and Python programming skills. It will also help to get an in-depth understanding of big data and analytics, NLP, image processing, and computer vision. Fortunately, the internet is rich with free resources that self-learners can take advantage of, from machine learning basics to complex AI techniques, blogs to ebooks, videos to real-life case studies. There also is a growing list of offline bootcamps and online courses you can turn to for more structured learning plans that will help you keep your skills up to date.

Stay on Top of Trends: Even if you haven’t used some of the skills mentioned above in a while, it’s critical to always keep yourself up to date on the latest industry trends. What skills should you brush up on? You can find out by signing up for AI-focused newsletters, following AI experts on social media, and reading machine learning blogs. Furthermore, as this industry vertical evolves, you will have more opportunities to diversify and focus on your specialty. Both data science and artificial intelligence are constantly changing, so staying looped into the musings of some of the top minds and pioneering thinkers in AI is vital.

Final Thoughts

Machine learning engineers are highly sought after, and the salaries reflect that. In the U.S., markets with the most opportunities are in Silicon Valley on the West Coast and in New York City on the East Coast, but there are areas throughout the country with highly competitive scenes. And employers also are open to providing opportunities to remote workers and digital nomads.

To bridge the skills gap, companies also are bringing on experienced tech professionals who add machine learning expertise to their resumes via offline bootcamps and online courses. So whether you’re certified or self-taught, there are plenty of opportunities up for grabs.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!