Machine learning is an AI branch that focuses on developing systems that can perform specific tasks and improve themselves automatically without requiring human intervention. Machine learning has become one of the most popular tech skills in the market.
The professionals who primarily help companies in developing and implementing machine learning-based solutions are machine learning engineers. Companies rely on them for handling their AI and ML requirements. Due to this, their salary is sky-high.
The following points will throw light on the average machine learning engineer salary, what factors affect it, and how you can enter this sector. Let’s get started!
What is the average machine learning engineer salary?
The average machine learning engineer salary in the US is $112,837 per year. Their pay starts from $76,000 per year and goes up to $154,000 per annum. Bonus for this role can go up to $24,000, and the shared profit can go up to $41,000. This role attracts such a high salary because while companies across the globe are looking for AI and ML professionals, their market supply is relatively low.
According to a Forrester report, AI and ML will generate new and innovative roles in multiple industries because companies would want to push AI to new frontiers. Companies would focus on implementing AI use cases faster to get ahead of their competitors.
Another reason why the demand for machine learning engineers is increasing is that more than a third of companies looking for adaptation and growth in 2023 will employ AI to solve their automation and augmentation problems.
Similarly, an Analytics Insight report found that the global skills gap in the AI sector is 66%. Certainly, there’s a shortage of skilled AI and ML professionals. That’s why the average machine learning engineer salary is substantially high all across the globe.
What does a Machine Learning Engineer do?
A machine learning engineer works with large quantities of data to create models that solve their organization’s particular problems. Their role is quite similar to that of a data scientist as both use large amounts of data. However, machine learning engineers have to create self-running solutions that perform predictive model automation.
Their created solutions learn from every iteration to improve their effectiveness and optimize their results to get better accuracy. Machine learning engineers have to program models that can perform their tasks with minimum or no human intervention. They work with data scientists to identify the requirements of their organization and create the required solutions.
Machine learning engineers usually work in teams. Thus, they must have strong communication skills. Machine learning engineers have to develop ML-based apps that match their client’s or customer’s requirements.
They explore and visualize data to find distinctions in data distribution that could affect model performance during a deployment. ML engineers are also responsible for researching, experimenting with, and employing the necessary ML algorithms.
They have to perform statistical analysis, find datasets for their training and train their ML systems as required.
Factors affecting the average machine learning engineer salary
Recruiters are always on the lookout for candidates that have the latest and in-demand skills. To get attractive pay as a machine learning engineer, you must stay on top of the industry trends and develop the necessary skills.
For example, the most popular skills among machine learning engineers in the US are deep learning, natural language processing (NLP), Python, and computer vision.
Having certain skills can help you get a pay bump. One such highest-paying skill for machine learning engineers in the US is Scala. ML engineers with the Scala skill earn 26% more than the national average. Other skills that offer help you get higher pay in this field are:
- Data modeling (16% more than the average)
- Artificial intelligence (11% more than the average)
- PyTorch (11% more than the average)
- Image processing (7% more than the average)
- Apache Spark (15% more than the average)
- Big data analytics (5% more than the average)
- Software development (3% more than the average)
- Natural language processing (3% more than the average)
Knowing which skills offer better pay can help you strategize your career progress and boost your growth substantially.
Experience plays a crucial role in determining how much you earn as a machine learning engineer. According to the statistics, entry-level ML engineers make 17% less than the average, while a mid-career professional in this field earns 21% more than the same.
Machine learning engineers with less than a year’s experience make $93,000 per annum on average, whereas those with one to four years of professional experience earn $112,000 per annum on average.
On a similar note, ML engineers with five to nine years of experience make $137,000 per year on average. Professionals with 20+ years of experience earn $162,000 per annum. As you can see, in machine learning, gaining more experience will help you bag higher pay.
Every city has a distinct culture, demographic, and cost of living. Hence, the city you work in can be a huge determinant of how much you make as a machine learning engineer. Several cities in the US offer significantly higher salaries than the average. Working there might help you get higher-paying roles in reputed companies as an ML engineer.
Cities with the highest average salaries for this role are:
- San Francisco (18% more than the national average)
- San Jose (16.9% more than the national average)
- Palo Alto (10% more than the national average)
- Seattle (7% more than the national average)
Similarly, you’ll find cities that offer below-average salaries for this role. These include Chicago (20% less than the national average) and Boston (8.9% less than the national average). You should always keep the city in mind while estimating how much you can expect to earn in this role.
Your machine learning engineer salary would vary from company to company. It depends on many factors such as the company’s size, its work environment, its offered benefits, etc. Companies that offer the highest salaries for machine learning roles are JP Morgan Chase and Co (average pay for this role is $137,344), Apple (average pay for this role is $129,149), and Amazon.com Inc (average salary for this role is $114,795).
Similarly, some companies offer lower salaries for this role due to their job requirements. Those companies include Lockheed Martin Corp (the average salary for this role is $104,228) and Intel Corporation (the average pay for this role is $92,964).
How to become a machine learning engineer?
Machine learning engineers are in high demand, and you can easily bag a job with lucrative pay in this field. To become a machine learning engineer, you must be familiar with the basic and advanced concepts of artificial intelligence, machine learning,
You must also be familiar with different machine learning tools and libraries so you can create ML models efficiently. The best way to learn these various subjects and develop the necessary skills for becoming a machine learning engineer is by taking an ML course.
At upGrad, we offer the Master of Science in Machine Learning and Artificial Intelligence program with the Liverpool John Moores University and the International Institute of Information Technology, Bangalore.
The course lasts for 18 months and offers 40+ hours of live sessions and six capstone projects. Some of the subjects you’ll learn during this program are statistics, exploratory data analytics, natural language processing, machine learning algorithms, etc. Each student will receive multiple benefits, including career coaching, interviews, one-on-one mentorship, and networking opportunities with peers from 85+ countries.
You must have a bachelor’s in statistics or mathematics with 50% or equivalent marks with one year of professional work experience in analytics or programming.
Machine learning is the skill of the future. ML technology allows companies to automate processes, develop better solutions, and advance their growth. Due to these reasons, the demand for machine learning engineers is increasing globally, improving the average pay for this role.
If you’re interested in becoming a machine learning engineer, we recommend checking out our Master of Science in Machine Learning and Artificial Intelligence program!
Which are the best US cities to work as a Machine Learning engineer?
Even though America’s Silicon Valley is still the top option for tech professionals specializing in AI and ML, today, there are many more places all over the US that are equally work-friendly. Firstly, Boston, with its abundance of world-reputed universities like Harvard and MIT, cybersecurity and insurance organizations, and startups is all set to become the top tech hub after Silicon Valley. The average salary offered in this US city ranges at 141,000 USD. Some other cities, as per data from Indeed USA, include San Francisco Bay Area (165,000 USD), Bellevue (149,000 USD), New York (138,000 USD), and Austin (167,000 USD) among others.
Can I get a job as a Machine Learning Engineer outside the USA?
Yes, certainly. Depending on your skillset, you can certainly bag rewarding jobs as an ML engineer all over the world. Some of the best English-speaking places where you can work as an ML engineer include London, which is considered the global melting pot of FinTech and AI, then Delhi, India, an excellent market that has always grabbed attention from international organizations. Next, Toronto, with its huge concentration of financial institutions, is a promising place for ML engineers as well as AI and data scientists. Apart from these, some non-English speaking countries include names like Paris, Montreal, and Geneva, among others.
Are machine learning and data science the same?
Data science is essentially all about systems and processes that can extract meaningful information using scientific approaches. Experts describe it as a combination of data modeling, IT, and business management, encompassing vast concepts. On the other hand, machine learning involves techniques utilized by data scientists that help machines or computers to learn from data and perform activities without human involvement. Interestingly, even though data science includes ML, it is phenomenally vaster than one can imagine, with striking differences.