Machine learning engineer salary: How much do AI techs make?
OpenAI’s free and easy-to-use ChatGPT helped to popularize the use of artificial intelligence (AI) among consumers.
ChatGPT and other large language AI models are powered in part by machine learning, a subfield of AI that makes it possible for computer programs to learn through the use of algorithms and statistical models.
As companies such as Alphabet’s Google (GOOG) and Microsoft (MSFT) continue to make headway in the development of AI, they hire hundreds of engineers specializing in machine learning. As demand for AI in popular, consumer-facing applications continues to grow, demand for machine learning engineers increases proportionally.
Pay can be very lucrative for the AI techs that create, train, and maintain machine learning programs. But just what exactly is machine learning, how can people find work in this rapidly growing field, and how much do machine learning engineer positions pay?
Related: How to invest in AI: From individual stocks to ETFs
What is machine learning?
Machine learning is a field of AI in which algorithms and statistical models are used to allow computers to learn from patterns and relationships in large sets of data such that they can formulate predictions or make decisions.
In ChatGPT, for example, the program learns from prior text to form succeeding text to create a complete sentence. Over time, the program receives a large number of similar inquiries, allowing it to learn from previous inputs and improve its responses.
Machine learning engineer positions typically require a background in computer science, mathematics, or a related field.
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Every time you use Apple’s (AAPL) Siri assistant to make an inquiry or engage the facial recognition software on your iPhone, the machine-learning algorithms that power these features incorporate this new data to improve future responses.
Similarly, Meta Platform’s Facebook (META) utilizes machine learning to monitor users’ activities to improve the individualization of its targeted advertising.
Machine learning has a wide range of applications and is used in many industries, including finance, healthcare, entertainment, and publishing. Automobile manufacturers like Tesla (TSLA) even use machine learning in the development of autonomous driving features in vehicles.
How much do machine learning engineers make?
Machine learning engineers are among the highest-paid professionals in the technology sector.
Jobs listing provider Indeed, as of May 2024, put the average annual base salary at $165,685, with a range of $108,162 to $253,800.
Meta tops the list of companies with the highest pay among Indeed users surveyed, at $217,441. That compared with $192,960 at Adobe (ADBE) , $189,683 at Apple, and $158,753 at Google.
Other companies offer even higher pay to machine learning engineers. On jobs site LinkedIn, as of May 2024, Netflix (NFLX) set a base pay range of $170,000 to $720,000, while DoorDash’s (DASH) was $140,100 to $210,100.
By comparison, the average pay for other fields within tech tends to be lower, according to Indeed data: $124,337 for a data scientist, $105,459 for a software engineer, $101,519 for a research scientist, and $98,987 for an electrical engineer.
Pay tends to be the highest in the Silicon Valley area, followed by New York and Seattle, where salaries reflect the high costs of living in those places.
Typically, machine learning engineers with advanced degrees, such as a PhD or master’s degree, tend to command higher salaries than those without. However, experience in the field, technical expertise, and leadership skills can also boost an applicant’s starting pay.
Additionally, stock options, bonuses, and other benefits can significantly lift the compensation for machine learning engineers well above their base salary.
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How to become a machine learning engineer
Becoming a machine learning engineer typically requires a background in computer science, mathematics, engineering, statistics, or a related field because these academic tracks emphasize the types of skills necessary to develop the sorts of algorithms and statistical models used in machine learning.
A degree in one of these technical fields can prepare you for a career as a machine learning engineer, and some companies will even offer on-the-job training for those who don’t have a technical background but have the propensity to learn about machine learning.
Schools at the university level offer courses in machine learning, while some, such as the Massachusetts Institute of Technology and Columbia University, offer certificates and boot camps in machine learning. Carnegie Mellon University is one of the few schools to offer a degree in machine learning, though that is at the master’s level.
TripleTen is an online boot camp that offers courses to those who don’t have a background in computer science or a related field but want to learn about software engineering, quality assurance, data science, and business intelligence analytics — all fields that can help pave the way for work as a machine learning engineer.
Google also offers online courses at the basic and advanced levels on machine learning.
Which companies are leading the way in machine learning?
It should come as no surprise that the companies making the largest strides in machine learning are also some of the biggest names in technology. After all, these household names have the financial resources to pay for the most talented and experienced machine learning engineers.
Google is one major company at the top of the field in machine learning research through its Google Research unit. Machine learning is incorporated into many of the company’s consumer products, including Google Search, Google Chrome, Google Assistant, Google Translate, and Google Photos.
Microsoft has invested billions of dollars in OpenAI, and the software giant has incorporated ChatGPT’s functionality into its Bing search engine and put machine learning into use in Microsoft Office and Microsoft Azure, its cloud computing platform.
Amazon (AMZN) , IBM (IBM) , and NVIDIA (NVDA) are among other large companies that are investing heavily in AI and machine learning.
