Business Intelligence Consultant Salary Per Hour – Machine learning engineers are in high demand. And the salary and benefits packages of machine learning engineers reflect that. In fact, machine learning engineering is the best job in the United States, according to an Indeed study that analyzes average salaries and job posting growth between 2015 and 2018.
The average machine learning salary, according to Indeed research, is approximately $146,085 (an impressive 344% increase from 2015). The average machine learning engineer salary was significantly higher than the other tech jobs on the list. The job title “full-stack developer,” for example, came in third with an annual payout of about $114,316, a 206% increase over three years.
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Let’s zoom out and take a broader look at the machine learning engineer role before we dive deeper into machine learning salaries.
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If you’re looking for a more comprehensive look at machine learning career options, check out our guides on becoming a data scientist and becoming a data engineer.
It’s safe to say that machine learning is the fastest growing field in computer science today. The role can be described as a complex combination of data science and software engineering. This is because machine learning engineers are tasked with feeding the data into data models defined by data scientists.
According to Håkon Hapnes Strand , senior data science consultant at Webstep, “the role of a machine learning engineer is 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 of how and why they want to use machine learning. In addition, these companies are almost always data scientists as well, so they have defined the distinction between the two. Data scientist is an often poorly defined job title. He is usually an analyst who knows some programming and machine learning. A machine learning engineer is a full-blown software engineer specialized in machine learning.”
Machine learning engineers are also responsible for scaling theoretical data science models to production-level models that can handle massive amounts of data in real time. The ultimate goal here is to develop algorithms that will enable machines to analyze the information they collect, recognize patterns, enable deep insights, and make decisions based on their findings.
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However, on a typical day, machine learning engineers will use a variety of big data tools and programming frameworks to redefine the raw data collected from data pipelines. These are then fed into data science models to prepare them 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 entire process begins with the design of complex algorithms that enable machines to collect and recognize such patterns. That’s where hard-working machine learning engineers come in.
In recent years, the demand for machine learning engineer jobs has surpassed the need for data scientists. According to LinkedIn’s US Emerging Jobs Report, the demand for MLEs has increased nearly 10-fold since 2012.
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The global machine learning market is predicted to grow from $1.6 billion to nearly $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 fiercer, producing the best remuneration packages in the job market.
Your machine learning engineer salary will depend a lot on where you are based. Your experience and expertise are often secondary when you consider the impact of variables such as the high cost of living in cities like New York City and San Francisco.
So it’s no surprise that these cities are the highest paying locations in the United States for machine learning engineers. They also have the highest demand for careers related to artificial intelligence.
It’s important to note, however, that even if you’re making a lot more than your peers, for example, in cities located in the Midwest, you’ll be spending a lot more to live in a major tech hub .
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According to Indeed , the average salary for a machine learning engineer in Ohio is approximately $37.34 per hour. That’s about 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).
Understanding the importance of position, let’s explore how wide a machine learning engineer salary can range based on experience.
An entry-level machine learning engineer typically has 0-4 years of experience. It could be someone who just got out of college or someone who changed careers and landed their first machine learning job.
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Machine learning salaries generally range from entry level, but the average is around $97,090. However, if you consider potential bonuses and profit sharing, that number can quickly rise to $130,000 or more.
Mid-level machine learning engineers typically have 5-9 years of experience and earn an average salary of $112,095. When you add potential bonuses and profit sharing, that number can quickly rise to $160,000 or more.
Machine learning is not limited to technology companies. Almost everyone wants to take advantage of this new technology, so you can expect salaries to continue to rise for the foreseeable future. (See more on non-traditional industries leveraging AI here.)
Skills that could influence a mid-level machine learning salary (according to PayScale) should look very familiar:
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Senior machine learning engineers with more than a decade of experience are the unicorns of the industry. As a result, they also command the best remuneration packages in the field. If you are a senior machine learning engineer, you can expect an average salary of $132,500.
Senior machine learning engineers are often absent from medium and large enterprises. There aren’t many around them, so it’s usually the multinational companies that have the resources to hire them.
As you may have noticed, starting salaries for machine learning engineers are already at the top of tech pay. If you want to be in a good position 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 worth.
Intern: There aren’t enough machine learning professionals to meet the current demands of the industry. So if you’re still in school, you have a great chance to land an internship long before you graduate (the same is true if you’re already working but transferring from another field). An internship will not only help you gain the necessary knowledge and practical real-life experience, but will also make you highly attractive to potential employers. When you apply for entry-level roles, you’ll be able to target the upper end of the salary range. Additionally, if you impress your employer, they may hire you for a full-time role.
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Sharpen Your Skills: As you climb up the ladder, make sure you brush up on your C++ and Python programming skills. It will also help to gain a deep 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 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 above-mentioned skills in a while, it’s important to keep yourself up-to-date on the latest trends in the industry. What skills should you develop? You can find out by signing up for AI-focused newsletters, following AI experts on social media, and reading machine learning blogs. Additionally, as this industry vertical develops, you will have more opportunities to diversify and focus on your specialty. Data science and artificial intelligence are constantly evolving, so it’s vital to stay in the loop on the ideas of some of the leading minds and leading thinkers in AI.
Machine learning engineers are in high demand, and the salaries reflect that. In the United States, Silicon Valley on the West Coast and New York City on the East Coast have the most opportunity markets, but there are places around the country where scenes are very competitive. And employers are willing to provide opportunities for remote workers and digital nomads as well.
To bridge the skills gap, companies also have experienced tech professionals who add machine learning expertise to their resumes through offline bootcamps and online courses. So whether you are certified or self-taught, there are plenty of opportunities available to you.
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Since you are here… Are you a future data engineer? Explore our free guide to what a data engineer really does. When you’re ready to create a CV that will enable hiring managers, join our Data Engineer Bootcamp in partnership with Washington University in St.
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