AWS Certified Machine Learning salary: Trends & projections

Jeff Peters
May 6, 2024 by
Jeff Peters

Machine learning (ML) is an exciting and fast-growing career field, and as more industries utilize large data at scale, highly skilled machine engineers become even more important. Machine learning algorithms enable businesses to make data-driven decisions, offer personalization and automate and improve their processes. Plus, there’s a variety of creative applications for machine learning, like improving disease diagnosis in healthcare, building more accurate fraud detection in finance and improving threat detection in cybersecurity. 

Amazon Web Services (AWS) plays a critical role in powering the infrastructure and computing resources needed for scaled machine learning. AWS is the most popular cloud service thanks to its scalability, comprehensive security measures and a wide variety of services such as storage, databases, networking and more. Becoming skilled in AWS and machine learning is an excellent way to package your skills and build a long-term career in an exciting field. 

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Understanding the AWS Machine Learning certification 

AWS offers the AWS Certified Machine Learning – Specialty certification for data scientists or computer engineers who want to validate their skills in designing, implementing, deploying and maintaining machine learning solutions at scale. This specialty certification is one of the most advanced AWS certifications available. 

After successful completion of the AWS Machine Learning certification, engineers should be able to: 

  • Design, implement and deploy ML solutions on AWS 
  • Collect, clean and analyze data 
  • Utilize other AWS ML services like SageMaker, Comprehend and Rekognition 
  • Support compliance and security while also taking into consideration ethical concerns 

Factors influencing salary 

Many factors affect the salaries of machine learning engineers, data scientists, and computer programmers. Experience is extremely important, especially when it comes to having real-life applications related to implementing and deploying ML systems. Geographic location also has high salary variations, with major technology hubs and large cities often paying higher salaries but having a higher cost of living. 

As machine learning becomes more sophisticated and education becomes more formalized, continuous learning and development is also important. Certifications like AWS Certified Machine Learning can help you stay competitive in your field, get promoted, move along your ideal career path and increase your salary. 

Average AWS Machine Learning salary data and trends 

Machine learning is a high-paying, lucrative career choice in technology, but it’s usually not looked at as an entry-level role. Let’s look at data from popular job websites to better understand your potential AWS machine learning certification salary. 

  • Payscale lists the average base salary of machine learning engineers with AWS skills at $124,333. 
  • Salary.com lists a $105,652 average base salary for a machine learning engineer.
  • Glassdoor lists an annual base salary of $128,412 and an average additional compensation of $37,104, including bonuses and other additional pay. 

By taking the averages of this data, the rough total average salary for a machine learning engineer is $140,379. Again, exact compensation depends heavily on experience, industry and geographic location, and additional compensation like bonuses and stock options. 

Salaries by location 

The salary for a machine learning engineer can vary wildly by location, especially in major metropolitan areas. Some of this wide variation is due to a few different factors: 

  • The cost of living 
  • The job is fully remote, hybrid or in person 
  • The average pay in related sectors 
  • Potential competition with other high-paying employees in the area 

For example, according to Indeed, a machine learning engineer in Santa Clara, California, makes roughly $189,407 per year. In comparison, in Richardson, Texas, machine learning engineers make roughly $130,871 per year. Unsurprisingly, for machine learning engineers, the highest-paying cities will be around major metropolitan areas. 

  • Santa Clara, California; $189,407 per year 
  • New York, New York: $189,399 per year 
  • Nashville, Tennessee: $178,533 per year 
  • Washington DC: $177,793 per year 
  • Seattle, Washington: $175,200 per year 
  • Dallas, Texas: $150,725 per year 
  • Tampa, Florida: $135,588 per year 

Salaries by experience 

Like any other field, significant increases in experience often lead to substantial pay increases. For example, according to Glassdoor, a machine learning engineer with four to six years of experience makes roughly $50,000 more than an entry-level engineer: 

  • 0-1 years: $122,000 
  • 1-3 years: $146,000 
  • 4-6 years: $172,000 
  • 7-9 years: $193,000 
  • 10-14 years: $213,000 
  • 15+ years: $230,000 

As your salary increases, your title should increase as well. According to Payscale, a Senior Machine Learning Engineer has an average base salary of roughly $157,186. 

Salaries by industry 

Many industries hire machine learning engineers, and as their popularity and accessibility grow, more industries will likely dip their toes into their machine-learning endeavors. Software development and technology are undoubtedly popular and high-paying industries, but major global retailers and financial organizations are also hiring machine-learning engineers. Other popular industries include aerospace and defense, transportation and manufacturing. 

According to Glassdoor, machine learning engineers at: 

  • Booz Allen Hamilton makes an average of $122,633,  
  • Capital One makes an average of $149,201 
  • Spotify makes an average of $226,160 
  • Apple makes an average of $275,467 

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Career opportunities for AWS Machine Learning 

Take a look at some various job roles related to the certification (available at the time of this writing) for additional context: 

  • Machine learning engineer ($165,753 from Indeed):. The most popular job title for the certification is a machine learning engineer who designs, builds and deploys ML models. 
  • Data scientist ($124,300 from Indeed): Machine learning models require huge amounts of data, and data scientists are equipped to analyze large data sets and derive what matters. 
  • Solutions architect ($137,920 from Indeed): Especially with a specialization in AWS, a popular career choice is a solutions architect who designs and implements scalable learning architecture. 
  • Product manager ($120,293 from Indeed): Product managers lead and develop the roadmap for machine learning features and services. 
  • Artificial intelligence (AI) ethics consultant ($121,841 from ZipRecruiter): Another field growing in popularity, an AI ethics consultant guides the responsible use of machine learning and artificial intelligence. 

AWS career pathways and progression 

Specialization in the world’s most popular cloud service provider, AWS, is a smart choice. However, there are a variety of certifications to consider within the AWS ecosystem. Whether you’re just starting in cloud computing or want to create specialization for your career, AWS offers significant reach, a wide range of services, deep integrations with other partners, innovation and continuous training opportunities.  

Even if you choose to switch paths within your career — such as moving to information technology or cybersecurity — the AWS ecosystem provides a competitive edge within a rapidly growing field full of opportunities for pay growth and career advancement. 

For example, just one AWS career pathway could look like: 

  • Earning the Cloud Practitioner – Foundation certification to gain an understanding of AWS Cloud. 
  • Working towards a cloud administrator by earning your AWS Certified SysOps Administrator Associate 
  • Build a strong understanding of the AWS Well-Architected Framework by earning your AWS Certified Solutions Architect – Associate and the AWS Certified Solutions Architect – Professional 
  •  Moving into machine learning with the AWS Certified Machine Learning – Specialty certification 

Preparing for the AWS Machine Learning certification 

The AWS Machine Learning certification is not an entry-level exam, so this specialty certification requires a solid basic understanding of the field and some real-world applications and knowledge. AWS recommends having one year of experience developing, architecting or running ML and deep learning workloads.  

This 180-minute exam comprehensively covers data engineering, exploratory data analysis, modeling, implementation and operations, and overall business understanding. 

Training options 

Luckily, there are tons of different training options on the market to adequately prepare you for the AWS Machine Learning certification. From authorized live boot camps like Infosec’s AWS Certified Machine Learning Boot Camp to self-study, there are many different options to support your training. You can even study for free with various books, YouTube videos and other resources. 

The amount of time you’ll need to prepare will vary depending on your current experience and understanding of both AWS and machine learning. 

Certification domain and domain weights 

The AWS Machine Learning certification is meant to thoroughly assess anything you need to perform a skilled data scientist or similar role. It covers the following four domains: 

  1. Data Engineering (20%) 
  2. Exploratory Data Analysis (24%) 
  3. Modeling (36%) 
  4. Machine Learning Implementation and Operations (20%) 

Here are some other quick facts about the certification exam: 

  • 180 minutes 
  • The cost is roughly $300 
  • 65 questions in multiple-choice or multiple responses 
  • The exam is an online proctored test or done at a Pearson VUE testing center 

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AWS Machine Learning salary conclusion 

Continuous learning and development are critical in a fast-developing field like machine learning to reach the highest levels of pay and career development. The AWS Machine Learning certification is a globally recognized exam in a fast-growing field. For those serious about a career in machine learning, it’s an excellent option to consider. 

To learn more about this certification, check our webinar on AWS certification training. 

Jeff Peters
Jeff Peters

Jeff Peters is a communications professional with more than a decade of experience creating cybersecurity-related content. As the Director of Content and Brand Marketing at Infosec, he oversees the Infosec Resources website, the Cyber Work Podcast and Cyber Work Hacks series, and a variety of other content aimed at answering security awareness and technical cybersecurity training questions. His focus is on developing materials to help cybersecurity practitioners and leaders improve their skills, level up their careers and build stronger teams.