Machine Learning Salary: A Guide to Machine Learning Engineer Salary Expectations, From Entry-Level to Senior

Written by Scott Wilson

robot hand grasping money

Machine learning engineers have landed in the sweet spot of the modern economy.

For the past decade, their expertise in developing algorithms to massage and analyze big chunks of data has made them the darlings of the data science industry. As a key technique in connecting and pulling insights out of Big Data, ML has enjoyed skyrocketing demand across industries.

It turned out that was just a preview, though. Over the past couple of years, a new suitor has appeared: artificial intelligence. As a technology that will be even more earth-shattering than data science has been, AI is suddenly on the minds of executives in every industry and in the strategic plans of every venture capital firm in the country.

All that investment has to go somewhere. A lot of it will end up in the pockets of expert machine learning engineers.

Pinning Down ML Engineering Salaries Is not an Exact Science

big data analyticsHow much exactly, though? It turns out that defining ML engineering jobs and finding average salaries for them can be tough.

First, those two big career path options, between data science and AI, aren’t well-established yet. Some sources lump the two together; others break them out in different ways. Even ML engineers working in the field may not be able to draw clear lines.

Next, the salary data sources don’t often reflect the specific job of machine learning engineer. Instead, these positions tend to get lumped in with a few similar roles in computer science and information technology.

The gold standard for identifying reliable salary estimates for any profession in the United States is the Bureau of Labor Statistics (BLS), which publishes fresh survey data each year. Unfortunately, BLS works like any other government agency: slowly. Although ML engineering has been a respected profession for quite a while now, there’s no distinct occupation tracked for the job.

In AI, ML Engineers May Find Three Basic Paths, Both in Education and in Their Careers

As if the situation weren’t already complicated enough, there’s something else that ML engineers working in AI need to consider. The field of AI and the educational programs that are feeding into it are tracking toward three general career paths:

  • Computer-science focused researchers and core system developers, working on new experiments and breakthroughs in AI.
  • Business-oriented engineers and application developers, working on applied uses for AI in corporate and government roles.
  • Specialist positions in areas of complex expertise where AI will have a major impact, everything from medicine to high finance.

Each of these paths also has separate salary expectations. But other than major colleges and universities, few agencies are parsing ML engineers according to these groups, so salary data is all but impossible to determine.

What Sort of Job Titles Do ML Engineers Have?

computer engineerThat leads to another consideration, which is the job titles that ML engineers work under.

Many ML engineers are considered a kind of crossbreed between data scientists and software engineers.

As you might have found on our machine learning careers page, that dual nature means positions get listed under all kinds of different titles:

Even in the best salary data sources, there’s some slippery ground between what we are calling an ML engineer and what the exact job title looks like.

The closest fit within the universe of official BLS occupations is with Computer Information and Research Scientists and Data Scientists, but some also get filed under Computer Programmers or Software Developers, Quality Assurance Analysts, and Testers.

Naturally, that tends to dilute the data. High-paying ML engineer jobs go in the same bucket as website UI testers. It drags down the averages.

So we hedge a little bit with the BLS data and look only at the reported top ten percent in each role. That produces these figures for 2022, which represent the floor for the 90th percentile. That means these job titles come with salaries that pay at least what you see here:

These figures lump together data science and AI, among other fields, so you don’t get any kind of picture of the salary differences between the two primary paths that ML engineers follow, either.

Still, this is an industry that is all about data. And these are jobs that are all about figuring out how to parse that data. So dive into what we’ve found and see what you can earn as an ML engineer!

How Education Effects Machine Learning Engineer Salaries

student engineers assembling roboticsWhen we say expert ML engineers, we do mean expert. Machine learning, particularly in the field of artificial intelligence, is a complex science. Most job listings in this category are looking for talent with graduate degrees in the field, or years of practical experience under their belt. According to the 2023 Data Science & AI Salary Report published by executive talent recruiting firm Burtch Works, 72 percent of all data scientists and AI professionals had a master’s or doctoral degree.

You can expect to see compensation that is commensurate with those years of study and practice, though.

What if you haven’t put in the time to earn a master’s degree in machine learning yet? The good news is that demand is high enough that 31 percent of data science and 17 percent of AI professional positions are open to bachelor’s holders.

Machine Learning Engineer Salaries by Level of Education

BLS also doesn’t do any salary breakdown by level of education. For that, we must turn to Burtch Works again. They break out salaries according to degree, responsibilities, and by either data science or AI roles. We’ll give you the range for each degree level, showing the median for entry-level and senior roles at each end of that range:

    • Bachelor’s $88,300 – $150,000
    • Master’s $100,000 – $158,000
    • PhD $119,000 – $160,000
    • Bachelor’s $95,100 – $160,000
    • Master’s $115,100 – $160,200
    • PhD $117,650 – $175,000

Management positions, of course, pay even more, ranging from $145,200 all the way up to $300,000.

Salary Ranges for Machine Learning Engineers Can Differ by Industry

construction engineersThe difficulties in pinning down ML engineer salaries extend into trying to differentiate by industry of employment, too.

Every different industry has different requirements for AI and data science. They also come with different levels of competition, profit margins, and the overall ebb and flow of demand. ML engineers may need certain specialized knowledge or training that is in short supply in the market, and those that have it can practically name their salary.

All of that inevitably plays into the typical salary that ML engineering roles in a given industry will pay. Figuring out what those salaries are is another matter.

Machine Learning Engineer Salary Levels by Industry

ai business innovationBLS keeps great data on salary levels by industries. But it’s muddied by the fact that the occupations don’t neatly line up with ML engineering jobs in the first place.

There’s another challenge, which is that both AI and data science are sometimes seen as distinct industries, but in the real world, they are usually fields that may have positions in almost any industry. That’s been increasingly true as both technologies have found new and innovative uses in almost every type of company.

But we can try to tease out some data from some of the industries where AI and data science have both been making the biggest impact in recent years.

Computer Systems Design and Related Services

Companies in this sector are those that fall toward the top of the pure research and design of AI. ML engineers here push the boundaries of the possible with new deep learning and algorithm development.

Software Publishers

Big vendors responsible for bringing AI middleware systems to the economy live here. ML engineers will spend their time working through scaling and software engineering considerations… and they’ll be well-paid for their efforts, since this is the most lucrative industry for these positions.

Finance and Insurance

This takes in the big banks, investment and trading firms, and major insurance companies interested in using ML for statistical analysis.

hallway meeting

Federal, State, and Local Government

Government labs take care of ML development in the sensitive realm of defense and intelligence, but also adapt AI and data science techniques to the business of government.

Arts, Entertainment, and Recreation

One of the biggest new markets for ML engineers may be in arts and entertainment. As publishers and filmmakers go all in on effects and post-processing based in AI, demand for ML engineering in this industry is likely to rise.


Whether AI is running the show or not, this sector will always be around. These are the companies that make the stuff the world depends on. Specialists in robotics and control systems are a hot commodity in all types of industrial manufacturing companies.

Legal Services

Specialized legal service providers in this industry summarize cases, process masses of documents, and handle various outsourced legal operations. It’s easy to see why ML engineers specialized in data science are in-demand here.

Architectural, Engineering, and Related Services

Corporations in this sector are responsible for designing and building everything from infrastructure to ski resorts. Machine learning engineers will contribute to the planning and execution sides of the business with robotics integration, design optimization, and automation.

Scientific Research and Development in Physical, Engineering, and Life Sciences

As you can see, not every industry has high enough employment levels in each category to give BLS something to work with. But you can get a good perspective of the median salaries. Still, keep in mind that ML engineers are likely to make more, and that those categories take in many jobs that have nothing to do with ML engineering.

Burtch Works has their own industry breakdowns, but also segmented between data science and AI roles.

For the mean salaries at a mid-career level, their data shows these rates by field:

Data Science

Artificial Intelligence

Your Location Can Make a Big Difference in Salary Expectations for Machine Learning Engineers

smart farmingAlthough the truth is that most actual horsepower engaged in machine learning happens in a few hidden cloud computer data centers in remote parts of the country, the actual workplaces of ML engineers are still in research labs and physical office spaces in every region of the U.S.

With big differences in cost of living, industry concentration, and research and educational access, that can lead to ML engineer salaries that differ by region, too.

Burtch Works again offers a breakdown between data science and AI professionals, grouped by job level and experience. For the intermediate career professionals, they found these median base salaries:

Data Science

Artificial Intelligence

Anyone paying even a little bit of attention to the tech industry isn’t going to find anything surprising in those numbers. Hotspots in tech strongholds like California, Washington, Texas, and New York help spike salaries in the Mountain West, the West Coast, and the Northeast. Surging development in the Atlanta area may be increasing rates in the Southeast in AI roles for ML engineers.

A more fine-grained look comes from BLS. Although, again, they may not have an exact match in machine learning engineering roles, taking the median annual salary for computer and information research scientists for a few select metro areas around the country tells a story of comparative wages:

Of course, the increasing number of machine learning engineering jobs that can be performed online are resulting in more general shifts in the relationship between salary and location. Some companies may shift their base salary rate down for staff located in areas with a lower cost-of-living.

But despite big shifts in the post-pandemic work environment, all these trends are still in flux. You can expect it to take more time for expectations to settle out. But you can also be sure that a solid education and qualifications in machine learning give you a strong hand in salary negotiations for any position.

Machine Learning Engineer Salary Rates Are Often Enhanced by Bonuses

robot developmentBase salaries for machine learning engineers are counted among the top in the American job market today. But those are really just the entry point for hot jobs inside the AI tsunami zone.

Everyone dreams of landing a role at the next OpenAI or Anthropic, the hot startup that will crack open the next great technology in thinking machines. While starting salaries may be lower than at established tech titans like Apple, Meta, or Google, generous stock options are common perks in startups. While they may expire without value if the company itself goes down, they may also explode wildly if an IPO finds favor in the market.

The typical Silicon Valley benefit package comes with all kinds of enticements to keep you onboard and grinding. Even at more established companies, salaries for machine learning engineers are augmented by bonuses and other perks.

Good old-fashioned cash bonuses also play an important role. According to Burtch Works, median bonus percentages for data science professionals run from 12 percent up to 25 percent. In the world of AI, the generosity rises even higher, running from 13 percent up to 33 percent… typically increasing at higher seniority and responsibility levels.

Of course, machine learning engineers have a versatile skillset that allows them to either shoot for the moon or play it safe back on earth. Anyone who has solid skills in machine learning will find a wealth of stable positions in a wide range of industries that aren’t all about changing the world. Keeping the world running normally will still keep a roof over your head, with a lot more stability in the bargain.

2022 US Bureau of Labor Statistics salary and employment figures for Computer and Information Research Scientists, Data Scientists, Computer Programmers, and Software Developers, Quality Assurance Analysts, and Testers reflect national data, not school-specific information. Conditions in your area may vary. Data accessed December 2023.