Masters in Artificial Intelligence Salary: The Low-Down

Published on: Oct 11, 2022
Last Updated on: Nov 6, 2022 By: Editorial Staff

It’s hard to go through a day without touching apps built in the early stages of artificial intelligence: from personalized content streams online, to the weather report, to the logistics that bring your Amazon order to your doorstep, AI is becoming a constant partner.

According to Bloomberg, this influence will only grow in the future. Citing a Zion Market Research study, Bloomberg reports that the global AI market, currently valued at $59.67 billion, will grow at a compound annual growth rate of 39.4% to $422.37 billion by 2028 — bigger than the 2022 GDPs of Austria, Ireland, and the UAE.

global AI marketplace sizing data visualizaiton

Image Source: ZionMarketResearch.com

This growth is attracting vast numbers of entrants into AI master’s programs, with many universities in turn expanding their AI capacities. According to Stanford’s AI Index, 73 leading universities reported increasing graduate offerings teaching practical AI skills by over 40% during the four academic years leading up to 2020, with faculty specializing in AI growing by almost 60% over the same timeframe. This growth is likely to continue over the next decade.

Entrants to these programs cite not only a booming job market but extremely high salaries as reasons for matriculating — but how much can you expect to earn with a master’s in artificial intelligence salary? In this article, we’lll break down expected compensation for a variety of advanced artificial intelligence jobs before providing some background on how you can start down the path to earning your own.

What salary can you expect to earn with a master’s in artificial intelligence?

According to PayScale, the average salary for someone holding a master’s degree in artificial intelligence is $102,848 — but the actual salary for AI professionals varies depending on several factors, including job title, specialization, location, and employer.

Based on PayScale’s data, the jobs with the highest paying AI salaries are:

Artificial intelligence engineers, who reportedly earn an average of $171,715, with top earners in the same profession earning upwards of $257,530 in annual salary.
Research scientists with AI skills, who reportedly earn an average base salary of $134,456.
Senior data scientists, who reportedly earn an average base salary of $117,438 and up to $150,000.
Machine learning engineers, who earn an average base salary of $106,055 and up to $150,000.

Factors that affect your salary when you have an MS in artificial intelligence include:

Skills: Having skills in subdisciplines like machine learning, robotics, and computer vision or with software like Python, Java, R, ApacheSpark, or Amazon Web Services can increase your competitiveness and salary potential from AI jobs.
Experience: AI specialists with more professional experience and seniority tend to attract more money in salaries than those just starting in the field.
Location: AI specialists working in locations like New York, Washington DC, San Jose, Seattle, and San Francisco on average earn higher salaries due to the higher cost of living and greater demand.
Specializations and additional certifications: Some industries, such as finance and government, require additional skills, subject matter expertise, or security clearances. Those with these extras will receive higher salaries in return.
Employer: Big tech companies competing for top artificial intelligence specialists offer higher salaries and attractive benefits for people with in-demand AI skills. Top employers include Google, Microsoft, and Facebook, among others.

We’ve covered the earning potential in various AI positions for those with master’s degrees in AI, as well as some factors that will impact this earning potential — but what exactly do you need to do to unlock it in the first place? In the next sections, we’ll explore just that: what a master’s in artificial intelligence entails, what kinds of people are good candidates for the degree, and what the day-to-day looks like for high-earning AI careers.

What’s a master’s in artificial intelligence?

A master’s in artificial intelligence is an advanced degree, usually offered by a computer science department, that’s intended to familiarize students with the landscape of artificial intelligence as well as give students the skills and other practical knowledge they need to undertake doctoral research or build and deploy artificial intelligence solutions in the industry.

Skills include:

Mathematics, especially statistics and linear algebra
Programming using languages like Python, R, and Java
Data pipeline skills such as data mining, data analytics, and data visualization
Ability to write artificial intelligence algorithms and build artificial intelligence models using software like Apache Spark, TensorFlow, and PyTorch.

Potential knowledge areas:

Machine learning, including deep learning and neural networks
Natural language processing (NLP)
Computer vision
Robotics

While you can expect most master’s programs in artificial intelligence to offer a curriculum more or less in line with the above, there are still significant differences in how programs design their curricula’s scope and structure.

At Northeastern’s Khoury College of Computer Sciences, for example, graduate students in the MS in artificial intelligence program first develop a comprehensive knowledge of artificial intelligence before choosing a specialization in robotics, machine learning, computer vision, intelligent interaction, or knowledge management. At Boston University, master's students have the opportunity to pursue independent projects, such as an MS thesis that they publicly defend in their final semester.

Northwestern University’s McCormick School of Engineering takes an interesting approach by offering dual tracks, one for those who plan to continue in artificial intelligence and one for those with advanced degrees who plan to take what they learn and return to their home discipline. In the former, MSAI, students will complete an internship with one of Northwestern’s industry partners or work on a project in Northwestern’s artificial intelligence laboratory before finishing off with a capstone project in their final semester. In the latter, MSAI+X, students will start with a programming and math bootcamp while foregoing these later components.

Who’s a master’s in artificial intelligence for?

Northwestern’s bootcamp requirement for students without a computer science or math background is instructive.

In general, applicants to artificial intelligence master’s programs are expected to have gotten their bachelor’s degrees in computer science, mathematics, or another technical field.

Though they don’t have a parallel track for students without a CS background, Northeastern similarly requires that incoming students have a strong background in computer science and mathematics. This background can be demonstrated by passing two placement exams on the fundamentals of computer science and statistics, probability, and linear algebra, respectively, or acquired before beginning study through the completion of two introductory courses.

Philadelphia’s Drexel University follows suit, requiring that students enter their Master’s in AI and Machine Learning program with a four-year bachelor’s degree or master’s degree in computer science, software engineering, or a related stem field with relevant work experience.

What kinds of careers are open to graduates of AI master’s programs?

We’ve already covered how much artificial intelligence engineers, research scientists, data scientists, and machine learning engineers make — but what exactly do they do with their master’s educations?

Artificial Intelligence Engineer

Artificial intelligence engineers focus on developing artificial intelligence models that can be deployed in real-world contexts — though the professionals with these responsibilities often have job titles like “software engineer,” “AI specialist,” and “machine learning engineer.” And then, of course, there are the more specialized AI engineer positions like “robotics engineer,” “big data engineer,” and “business intelligence developer.” But regardless of the title, the core is the same: to harmoniously integrate artificial intelligence into human life to drive value both for the companies providing the AIs and the users interacting with them.

AI Engineer - Computer Vision/Health Imaging @ Covera Health

At Covera Health, an entry-level AI engineer working in computer vision and health imaging has the opportunity to leverage AI solutions to ensure accurate diagnosis within the field of radiology, ensuring people have access to quality care regardless of location.

Key responsibilities include:

Consulting on Amazon Web Services (AWS) platform optimization
Designing, developing, and shipping AI models to maintain and improve existing analytics
Communicating results to internal and external stakeholders

Requirements include:

Bachelor’s or master’s degree in computer science or a nearby field
1-3 years of experience (including internships) as a software or AI engineer
Experience with SQL, a programming language like Python or C++, and Unix environments
Experience with machine learning models, deep learning pipelines, and, ideally, deep learning environments inference engines like TritonRT

On Glassdoor, an AI Engineer at Covera Health — a small health services company — earns $137,022 annually.

Data Scientist

Data science isn’t normally considered a subdiscipline of artificial intelligence, but rather a discipline unto itself, albeit with some overlap. Regardless, the data scientist is frequently included among top artificial intelligence careers. In terms of responsibilities, a data scientist ideates and executes novel approaches that turn raw data into business insights and solutions. After understanding business needs, they determine what types of data are relevant in addressing those needs and what kinds of questions need to be asked of this data, and then help develop machine learning models and other predictive analytics to efficiently carry out this analysis. After the analysis, a data scientist is usually responsible for communicating results to relevant stakeholders.

Principal Data Scientist, Mars

To get a glimpse into the qualifications and responsibilities of a data scientist, let’s take as an example a data science role based in Tennessee City, TN, at Mars, the candy and pet food conglomerate. Mars is looking for a principal data scientist with the following experience:

Previous experience using data science and/or data analytics to drive business change
Diverse technical experience with statistical analysis in a variety of industries
Fluency with the Python programming language and PySpark, an application programming interface (API) that brings Python together with the Spark big data analysis engine
Previous experience in consumer packaged goods (CPG), retail, or healthcare
Leadership, mentorship, communication, and critical thinking skills

At Mars, a qualified candidate would be responsible for the following:

Taking the lead on data science projects in their pet food division
Overseeing the work of junior data scientists
Implementing machine learning techniques and models to analyze data sets
Employing data visualizations to communicate findings
Ensuring constant focus on business goals across all data science projects

A candidate with a master’s degree hired for this position could expect to make at least $125,000 annually with benefits — an especially competitive salary when you take into consideration the cost of living in Tennessee.

Research Scientist

While there is considerable overlap between data scientists and research scientists — both require backgrounds in mathematics, programming, and data management — they differ in their research. While data scientists, as we’ve seen, are often squarely focused on business intelligence that can be immediately applied, research scientists often work on more theoretical problems, with less immediately applicable solutions.

Assistant Research Scientist, NYU Langone

An assistant research scientist working in computer disease simulation modeling at New York University’s Grossman School of Medicine offers a good example of how the role’s qualifications and responsibilities might differ from those of a data scientist. NYU expects a qualified candidate to have:

At least a master’s degree in a relevant discipline (ideally computer science, applied mathematics, or data science) with some real-life experience
Fluency with Python, R, C, or C++, with experience programming Markov modeling (a modeling approach geared toward randomly changing systems, like a body fighting disease)
Experience undertaking medical literature reviews
Background in both advanced statistics and epidemiology
Proficient communication skills

Among other things, a successful candidate would be expected to be responsible for:

Generating research ideas that build on existing research
Managing research projects
Developing research techniques, methodologies, and protocols
Assisting in gathering data and interpreting results of AI computer disease simulation modeling
Preparing data sets and research findings for publication, including data visualizations
Maintaining current subject matter knowledge in fields such as epidemiology and biostatistics

There is wide variation in research scientist salaries depending on the particular industry or discipline, but Payscale puts the average salary at $134,456 annually. Given that the NYU position is a more junior role in academic medical research (that still requires a master’s degree!), the starting salary would likely be lower than this average.

Machine Learning Engineer

A machine learning engineer is responsible for designing, developing, and shipping machine learning models—and then maintaining them once they are shipped and deployed to a live product. An ML engineer might work on a team focused on building a particular product or feature, or they might work as a generalist and move between a variety of different types of projects. In addition, machine learning engineers can be responsible for managing data systems and undertaking various kinds of data analysis, such as analyzing data sets from A/B tests on a machine learning system or algorithm. All this doesn’t take place in a vacuum. ML engineers are tasked with using machine learning skills to solve real-world problems, and so their efforts can have a measurable effect on the lives of real people.

Machine Learning Engineer, Glassdoor

For the exact skills needed, take as an example a machine learning engineer working in Chicago to power the search experience at Glassdoor, the job-search and review site. Glassdoor’s ideal candidate for this position has the following qualifications:

B.S. or above in Computer Science or related field
2 or more years of experience with the application of machine learning best practices and the deployment of machine learning models at a company
Hands-on experience using both structured and unstructured data to create machine learning solutions for real-world problems
Experience in natural language processing
Ability to self-start and own medium- and large-size products
Real-world experience with both supervised and unsupervised learning
Experience working with big data sets
Ability to communicate effectively both in writing and verbally

At Glassdoor, a successful candidate would be doing the following:

Collaborating with an engineering team to drive solutions for Glassdoor’s various job-search and review products
Using cloud-based software tools to develop scalable systems to enhance Glassdoor’s data pipeline and improve ML model training and testing
Developing algorithms to determine the quality of user-submitted reviews, salaries, etc.
Working across teams and overseeing projects end-to-end

A candidate hired for this position could expect to earn $129,300–$193,900 annually, with candidates holding a master’s degree entering at the high end of this range.

Next Steps

If you’re attracted by the salaries offered to those with master’s degrees in artificial intelligence and are keen to learn more, we have some great options for you!

If you’re looking to learn more about what a master’s in artificial intelligence entails, you can head to our deep dive on the degree.

If you’re ready to begin researching master’s programs, you can check out our program recommendations.

If you’re wanting to learn more about how artificial intelligence is making a difference in key industries, you can check out our articles on the role of machine learning in business and medicine.

Increasingly, the world will need the brightest minds working on artificial intelligence problems — so we encourage you to dive in, and we’ll be here to help along the way.