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Career Path And Salary Trends For Machine Learning Engineers In The USA

Wanna become a data scientist within 3 months, and get a job? Then you need to check this out !

What: This article provides insight into the career path and salary trend of ML engineers.

Why: To assist readers and students who are interested in pursuing machine learning engineering to decide on their career path.

The need for Machine Learning engineers has increased with technological advancement. Collaboration with technologies is essential to expand and provide unique services. For this, more businesses are hiring machine learning experts to make their products and services better.

As the field is exclusive, machine learning engineer jobs enjoy high salaries. Because it requires a high level of specialized education, a well-developed skill set, and a natural talent for the sciences. 

Person working together with robot

Source: Freepik

Who Are Machine Learning Engineers?

Machine learning engineers are experts who work with algorithms, data, and artificial intelligence. They have a strong knowledge of computer science, mathematics, and statistics. Similarly, ML engineers are experts in developing and implementing machine learning algorithms to solve complex problems and make decisions based on data. 

The ML Engineering profession is developing innovative solutions for industries, such as healthcare, finance, and technology. For this, they need to stay up-to-date with the latest technology.

Importance Of Machine Learning Engineers

Machine learning engineers play an important role in today’s technological age. They have a huge contribution to the recent and ongoing advancement of Artificial Intelligence. Their importance comes from their ability to create, develop, and use complex algorithms that let computers learn from data and make wise decisions.

Moreover, the development of predictive models is the most important contribution made by machine learning engineers. These models are used in areas like personalised recommendations, fraud detection, autonomous vehicles, and medical diagnoses. Therefore, to keep their solutions at the top of technical improvements, these engineers must keep up with the latest developments in machine learning techniques.

Career Path Of Machine Learning Engineers

Machine Learning Engineers are in high demand across various industries, and their career path offers promising opportunities for growth and advancement.

Here is a general overview of the career path:

  1. Acquire Education In A Related Field:

A Bachelor’s degree in a related field such as computer science, or data science is a minimum education requirement. A strong knowledge of computer programming, data science, and mathematics background is important to have a career in ML.

  1. Develop Foundational Skills:

A foundational skill in programming languages is important for ML engineers. Similarly, a Solid understanding of linear algebra, calculus, probability, and statistics is important for developing and optimizing machine learning algorithms.

  1. Seek Entry-Level Positions:

Entry-level positions involve working on projects under professionals. This also includes tasks such as data preprocessing, model training, and basic algorithm development.

  1. Build Your Expertise

While working under professionals, help to build your expertise. Build your expertise by working on machine learning projects and earn relevant certificates. This helps to shape your future in ML engineering.

  1. Take A Role Of Senior-Level Position:

Senior-level position involves taking the role of leadership in machine learning projects, mentoring juniors, and making important decisions.

  1. Progress Towards Advanced Roles

Advanced roles involve a high level of expertise and leadership, often overseeing multiple projects. It also involves providing strategic direction for the application of machine learning within a company.

  1. Entrepreneurship And Innovation

Some Professional ML Engineers choose their own startup. They start their own company as consultants, applying their expertise to various industries and projects.

Focus Areas Of Machine Learning Engineers

Data Preprocessing in Data Mining - A Hands On GuideMachine Learning Engineers focus on many areas to develop robust and effective ML applications. These applications address real-world challenges across diverse industries. Let us take a look at some of the focus areas mentioned:

  • Data Preprocessing
  • Feature Engineering
  • Model Selection
  • Hyperparameter Tuning
  • Model Training and Evaluation 

Job Categorization Of Machine Learning Engineers

Machine learning engineers are highly skilled programmers. They develop AI systems that use large datasets which can learn to make predictions.

Here are some job categories for ML engineers:

  • Data Scientists

Data Scientists are responsible for collecting, cleaning, and analyzing data to extract meaningful insights. They use their skills in statistics, programming, and data mining to develop predictive models and identify patterns in data. 

  • Research Scientists

Research scientists are responsible for conducting research in machine learning. They develop new algorithms and techniques for machine learning and test these algorithms on real-world data.

  • Software Engineers

Software Engineers are in charge of designing, developing, and testing software applications. They use their skills in programming, software designing, and debugging to build software that is reliable, scalable, and secure

  • DevOps Engineers

DevOps Engineers bridge the gaps between developments and operations. They automate and streamline the process of software development and deployment. 

  • Automation Engineers 

Automation engineers work in data teams and engineering teams. Their responsibility lies in automating the creation of machine models and applications.

Salary Of Machine Learning Engineers In The USA

The machine learning engineer’s salary varies based on factors such as industry, location, position, and more. 

Here is an overview of the affecting factors:

  • Salary By Industry

Salary in this field tends to vary according to industry. Some pay better than others. 

  • Real Estate: $187,938. (13 percent higher than other industries.)
  • Information Technology: $181,863. (10 percent higher than other industries.)
  • Media and Communication: $161,520. (1 percent lower than other industries.)
  • Retail and Wholesale: $157,766. (3 percent lower than other industries.)
  • Healthcare: $148,971. (9 percent lower than other industries.)
  • Salary By Location

Machine Learning Engineer Salary: Trends in 2023 -

A salary can depend on where the job is located.

  • New York, NY. $205,044 per year
  • San Francisco Bay Area. CA. $193,485 per year
  • Austin, TX. $187,683 per year
  • Cupertino, CA. $187,531 per year
  • San Diego, CA. $146,262 per year
  • San Francisco, CA. $143,125 per year
  • Dallas, TX. $143,107 per year
  • Bellevue, WA. $132,192 per year
  • Durham, NC. $104,834 per year
  • Salary By Position

Here is a machine learning engineer’s average salary by position.

  • Entry level (less than one year ): $80,000 to $120,000 per year
  • Mid Level (1-4 years): $100,000 to $150,000 per year. 
  • Experience (5-9 years): $120,000 to $180,000 or more per year.
  • Senior (10-19 years or above): $150,000 to $200,000 or above per year 
  • Salaries Of Positions Similar to Machine Learning Engineers

Here is a sample of average annual salaries of Positions Similar to Machine Learning Engineers

  • Data Scientist. $145,668 per year
  • Data Engineer. $134,296 per year
  • Deep Learning Engineer. $161,821 per year
  • Software Engineer. $119,348 per year
  • Computer Vision Engineer. $109,394 per year
  • Data Analyst. $72,673 per year

The above-mentioned points are the salaries of machine learning engineers based on different factors.

The Future Of Machine Learning Engineers

Why Invest in AI? All about the core technologies! | Henrik Christensen's  BlogThe future of machine learning engineers is influenced by many significant trends. The demand for intelligent systems and data-driven decision-making grows across various industries. Due to this, the experts at CDR Report Writers expect ML Engineers to play a major role in developing solutions. 

According to Maximize Market Research, the global ML market size is expected to reach $410.22 billion by 2029, growing at a CAGR of 46%. This shows that the scope area of machine learning engineers has been growing in an effective manner.

Here are some aspects that influence the future of machine learning Engineers:

  • Increasing Demand 

Machine learning engineers are growing rapidly. Organizations across industries have recognized the transformative power of machine learning. This demand will drive up salaries and open up new opportunities for career advancement.

  • Specialization In Domain-Specific Applications

Machine learning engineers specialize in applying their expertise to specific domains. This specialization leads to deeper insights and more impactful solutions for real-world problems.

  • Focus On Explainable AI

Machine learning engineers are focused on explainable AI. They aim to make these models more transparent and understandable. ML engineers play a crucial role in developing and implementing explainable AI techniques to build trust and confidence in machine learning systems.

Wrapping Up 

In conclusion, the field of Machine Learning Engineering offers a dynamic and rewarding career path. However, to be an ML Engineer, individuals need a strong educational foundation such as a comprehensive MS in Machine Learning, practical experience, and continuous skill development.

To get high salaries in the field of ML, engineers require a unique skill set, to adapt to the evolving technological landscape. The role involves collaboration with various technologies and plays a pivotal role in advancing machine learning and artificial intelligence.   

In today’s era, ML Learning is important in creating a predictive model for applications like personalized recommendations, fraud detection, and medical diagnoses underscores their significance.


  1. Machine Learning Engineer Vs Data Scientist?
Basis Machine Learning Engineer Data Scientist
Primary Focus Design and implement machine learning models Extracting insights and knowledge from data
Skill Set Strong programming, software engineering, and ML expertise Statistical analysis, data visualization, and machine learning
Data Handling Emphasis on handling large-scale data for model training Skilled in data collection, cleaning, and analysis
  1. What Programming Languages Are Essential For Machine Engineering?

Machine Learning Engineers work with many programming languages. The choice often depends on the specific tasks, preferences, and the machine learning framework being used. 

Some important programming languages for Engineer Machine Learning are:

  • Python
  • R
  • Java 
  • C++
  • Julia
  • Scala
  1. What To Include In Machine Learning Engineer Resume?

A good resume shows skills, experience, and achievements. Some of the essential components to include in your Machine Learning Engineer resume are:

  • Contact Information:
  • Summary or Objective:
  • Skills Section:
  • Education:
  • Professional Experience:
  • Projects:
  • Publications and Research (if applicable):
  • Certifications:
  • Awards and Honors:
  • Professional Memberships:
  • Languages (if applicable):
  • References:
  1. What Is The Difference Between An MLOps Engineer And A Machine Learning Engineer?
Basis MLOps Engineer Machine Learning Engineer
Primary Focus Deployment, automation, and operationalization of machine learning models in production environments. Development and implementation of machine learning models for solving specific tasks or problems.
Responsibilities Model deployment and monitoring. Data preprocessing and feature engineering.
Skill Set Strong knowledge of ML frameworks and tools. Deep understanding of machine learning algorithms and techniques.

Wanna become a data scientist within 3 months, and get a job? Then you need to check this out !