Artificial Intelligence Salary Package in United States
Artificial intelligence salary in US depends upon years of experience, profile, expertise, and diverse other factors. Here are the three levels on which AI salary is based upon.
Entry-Level AI Engineer Salary
This category is for freshers who are yet to gain ample knowledge and experience in the field. Their primary focus is to understand and learn fundamental concepts. Artificial intelligence has a high demand; hence leading companies are always on the lookout for skilled interns.
As this job role still has plenty of room for improvement, the artificial intelligence salary for entry-level engineers is the lowest among all. Artificial intelligence salary in US for freshers is approximately $115,000 per year.
Mid-level AI Engineer Salary
Mid-level artificial intelligence engineers are skilled in this field and tend to work under the same corporation for years. They are more equipped than entry-level engineers, so their pay scale is much larger than entry-level employees. AI salary in US for mid-level engineers is approximately $146,664 per year. These clusters of people are stable yet evolving individuals.
Senior-level AI Engineer Salary
Senior-level artificial intelligence engineers are the highest paid among all. They are independent and collaborative, with enhanced skills and experience. As they are senior-level engineers, they have 4-7 years of experience in this field. Artificial intelligence salary in US for experienced engineers is approximately $205,000 per year.
Top 5 Job Roles and Salaries of Artificial Intelligence
Artificial intelligence salary in US for freshers and experienced candidates also vary depending on their job profile. Here’s a list of top AI jobs and their offered salary figures.
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Machine Learning Engineer
A machine learning engineer focuses on researching and designing self-running software that will ease machine learning initiatives. These engineers are in massive demand by companies.
They are responsible for working in areas such as preventing fraud, managing risks, and customer insights. If you are interested in becoming a successful machine learning engineer, mathematics, programming, and computing are some must-have skills.
An average salary of a machine learning engineer is $107,635 per year. A master’s or doctoral degree in mathematics, Computer Science or AI is mainly preferred for this job. Though, deep knowledge of machine learning algorithms and neural networks is considered to be additionally valued.
Data Scientist
The responsibility of a data scientist is to use their expertise in mathematics, computer science, and statistics in order to analyze complex data. Data scientists gather data from various sources to gain constructive inferences and make predictions depending on past and present data information.
Data assessment further helps them to analyze the cause of the drawbacks in the business and, at the same time, how to overcome them in the coming future. With the support of data scientists, a business can perform positively. The average salary of a data scientist is $97,546 per year.
If you want to build your career as a data scientist, you must hold an advanced degree in Computer Science. The applicant must hone proficiency in programming languages such as Python, SQL, or Scala.
Big Data Engineer/Architect
Big data professionals are responsible for various processes, such as the development, maintenance, and evaluation of a company’s data. Big data helps companies to improve their efficiency and profitability. Hence, big data engineers are required by companies in order to collect and maintain databases.
If you are keen to build your career in AI as a big data engineer, you must have proficiency in programming languages like Python and Scala. The students who have a Ph.D. in Math or Computer Science are given more preference.
This field is suitable for both freshers and experienced as it has a good pay scale compared to other jobs in artificial intelligence. The average salary of a big data engineer is $112,564 per year.
AI Research Scientist
These are the professionals who are experts in the field of deep learning, machine learning, and statistics. A research scientist is responsible for designing and analyzing the information. In order to work as an AI research scientist, you must have a master’s or doctoral degree in Computer Science.
The average salary of a research scientist is $82,803 per year . The field greatly values experience and offers exceptional opportunities to people with a significant experience in their candidatures.
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Business Intelligence Developer
Business Intelligence Developers are professionals who help increase a business’s profitability by analyzing data and utilizing the obtained insights to reap valuable results. They recognize various trends by designing, testing, and maintaining data storage systems. A Business Intelligence Developer’s responsibility is to find solutions for business intelligence to meet business needs.
The average salary of these experts is $98,816 per year. If you are interested in this profession, a degree in computing and engineering with experience in design, SQL queries, and data processing is essential.
Best Highest Paying Companies for AI Engineers
Here are the top companies that pay exceptionally higher artificial intelligence salary per month in US.
Google is one of the leading global technology corporations with great demand for artificial intelligence engineers. The AI engineer salary in US for Google employees is $236,388 annually.
Facebook (Meta)
Facebook is one of the leading social media platforms that recruits experienced candidates in machine learning and artificial intelligence. AI jobs salary offered by Facebook (Meta) is approximately $257,846 annually.
Apple
Apple is the biggest technological corporation worldwide. This company specializes in internet services and computer software. They recruit employees for the roles such as artificial intelligence team head, machine learning engineer, and many others. The average pay scale offered to AI engineers here is approximately $227,094 annually.
Amazon
The American e-commerce giant Amazon has expanded its service in digital streaming and artificial intelligence. Amazon offers an excellent salary to artificial intelligence engineers compared to other companies. It provides approximately $209,762 annually as its AI engineers salary in US.
Uber
Global transportation leader Uber has a solid team of AI professionals as well, which receives around $314,746 as their annual pay package.
What is Machine Learning?
Machine learning is a subfield of artificial intelligence dedicated to the design of algorithms capable of learning from data. It has numerous applications, including business analytics, health informatics, financial forecasting, and self-driving cars.
In 2022, machine learning skills are widely in-demand. On Microsoft’s career page , 21% of the open developer positions currently mention “machine learning”. On Amazon’s career page , it’s 63%.
According to the Future of Jobs Report published by the World Economic Forum, machine learning is expected to be one of the world’s most in-demand skills through 2025.
Course Ranking Methodology
To create this ranking, I followed a three-step process:
First , I’m a developer at Class Central , the leading search engine for online courses.
So I went through our catalog of over 50K courses to put together a preliminary selection. I did so by taking into account factors like reviews, ratings, enrollments, bookmarks, and more.
So this was a rather objective step: I narrowed down the options by looking at well-defined metrics.
Second , I used my experience as an online learner to evaluate each preliminary pick.
Metrics such as course ratings rarely tell the whole story. I’ve completed many MOOCs , earned an online bachelor’s in computer science , and I’m enrolled in Georgia Tech’s online master’s in computer science ( OMSCS ). This has given me some perspective on what to look for in an online course, which I used to evaluate each of my preliminary picks.
So this was a rather subjective step: I combed through my picks to arrive at a near-final selection.
Third , I expanded this selection to include other valuable resources I’ve come across.
Since there are long-established courses in most topics, more recent courses on the same topic can go unnoticed. But sometimes, these are great. I made sure to include those when possible.
So this is a rather subjective step again: I rounded up my picks with excellent but less well-known courses.
The end result is a unique selection of courses that combines a decade of Class Central data and my own experience as an online learner to try to get the best of both worlds. So far, I’ve spent more than 15 hours building this list, and I’ll continue to update it.
Course Ranking Statistics
Here are some statistics regarding this course ranking:
- Combined, these courses have accrued over 6.6M enrollments.
- The most-represented course provider in the ranking is Coursera, with four courses.
- Combined, these courses have been bookmarked over 118,000 times on Class Central, while the machine learning subject itself has been bookmarked over 195,000 times.
- The most-popular course in the ranking has over 4M enrollments by itself.
- Eight courses are free or free-to-audit, while two are paid.
- Combined, these courses are received over 500 reviews on Class Central.
Without further ado, here are my picks for the best machine learning online courses.
My first pick for best machine learning online course is the aptly named Machine Learning , offered by Stanford University on Coursera.
This is the seminal machine learning course, and a very special course indeed. Taught by Andrew Ng , it was one of the original courses that kicked-off the popularization of massive open online courses (MOOCs).
Bolstered by the course’s success, Andrew Ng went on to cofound Coursera.
What You’ll Learn
This course starts by laying down the mathematical foundations of machine learning. It begins with a review of linear algebra and univariate linear regression before moving on to multivariate and logistic regression.
It then jumps from topic to topic each week to cover a wide variety of machine learning techniques and models. These include deep learning, support-vector machines, and principal component analysis.
Finally, it touches on practical aspects such as how to design and leverage large-scale machine learning projects.
By the end of the course, you’ll have a broad understanding of machine learning, its concepts, and its methods. You’ll be able to implement fundamental machine learning algorithms such as back propagation and k-means clustering.
You’ll be equipped to tackle tasks such as multi-class classification and anomaly detection. And you’ll be able to use Octave and Matlab to complete practical projects involving optical character recognition using a wide variety of approaches.
One Thing to Note
This course uses Octave rather than, say, Python. The course will teach you the concepts but not the tools most commonly used nowadays in machine learning. Despite that, it remains in my view the machine learning course of choice, hence its top spot.
But if you’re looking for a course more relevant to the day-to-day of a machine learning practitioner, check the next pick.
How You’ll Learn
The course is broken down into 11 weeks. Each week involves about 6 hours of work. Concepts are taught through a combination of video lectures and readings.
In terms of assessments, each week includes at least one auto-graded quiz. Most include several. And most weeks include a multiple-hours long programming project. There are 8 in total.
| Institution | Stanford University |
| Provider | Coursera |
| Instructor | Andrew Ng |
| Level | Mixed |
| Workload | 61 hours total |
| Enrollments | 4.5M |
| Rating | 4.9 / 5.0 (166K) |
| Certificate | Paid |
Fun Facts
- The course launched in October 2011 with over 100K learners , just two months after being announced. It was unprecedented for a course to have this many learners.
- With 4.5M learners, it has grown to become one of the most popular online courses ever, if not the most popular ever.
- Besides being a professor of computer science at Stanford University, Andrew Ng was the former Chief Scientist at Baidu and the cofounder of Google Brain.
- The course has amassed over 68,000 bookmarks on Class Central.
- While the course includes a paid certificate, the entirety of the course material, including all the assignments, can be accessed for free.
- The instant popularity of the course encouraged Andrew Ng to found Coursera and, more recently, DeepLearning.AI.
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