• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar

TechGuide

  • Degrees
    • Analytics
      • Analytics Associate
      • Online Bachelor's in Data Analytics
      • Online Bachelor's in Data Science
      • Data Analytics Master's
      • Data Science Master's
      • Business Analytics Master's
      • Online Master's in Business Analytics
      • Online Data Analytics Master's
      • Online Master's in Data Science
      • Data Science PhD
      • Data Analytics PhD
      • Business Analytics PhD
    • Computer Science
      • CS Associate
      • IT Associate
      • Computer Science Bachelor's
      • Artifical Intelligence Master's
      • Computer Science Master's
      • Machine Learning Master's
      • Software Engineering Master's
      • Online Associate Degree in Computer Science
      • Online Bachelor's in Computer Science
      • Artificial Intelligence Online Master's
      • Online Master's in Computer Science
      • IT PhD
    • Design
      • Graphic Design Associate
      • Graphic Design Bachelor's
      • Graphic Design Master's
      • Instructional Design Master's
      • Integrated Design Master's
      • UX Design Master's
      • Online Graphic Design Degree
      • Online Video Game Design
      • Online Master's in Instructional Design
    • Engineering
      • Civil Engineering Master's
      • Aerospace Engineering Master's
      • Electrical Engineering Master's
      • Engineering Management Master's
      • Industrial Engineering Master's
      • Mechanical Engineering Master's
      • Robotics Master's
    • Fintech
      • Fintech Associate
      • Fintech Bachelor's
      • Blockchain Master's
      • Fintech Master's
    • MBA
      • Business Analytics
      • Business Intelligence
      • Data Analytics
      • Data Science
      • Digital Marketing
      • Fintech
      • Information Technology
  • Careers
    • Analytics
      • Business Analyst
      • Business Intelligence
      • Data Analyst
      • Data Architect
      • Data Engineer
      • Data Scientist
      • Data Specialist
      • Sports Data Analyst
    • Computer Science
      • Artificial Intelligence
      • Cloud Computing
      • Computer Programmer
      • Computer Scientist
      • Front-End Developer
      • Full Stack Developer
      • Machine Learning
      • Software Developer
      • Software Engineer
      • Video Game Developer
      • Web Developer
    • Design
      • Data-Driven Designer
      • Graphic Designer
      • Instructional Design
      • Multimedia Artist
      • Product Designer
      • UX Designer
      • Video Game Designer
    • Engineering
      • Aerospace Engineer
      • Civil Engineer
      • Computational Science
      • DevOps Engineer
      • Electrical Engineer
      • Engineering Manager
      • Industrial Engineer
      • Mechanical Engineer
      • Robotics Engineer
    • Fintech
      • Fintech App Developer
      • Blockchain Developer
      • Compliance Expert
      • Cryptocurrency Analyst
      • Financial Analyst
      • Innovation Analyst
      • Investment Analyst
      • Investment Banker
      • Product Manager
      • Quantitative Analyst
      • Tech Sales
    • Marketing
      • Content Marketing
      • Content Strategist
      • Marketing Analyst
      • Social Media Manager
  • Certifications
    • Analytics
      • Business Analyst
      • Data Analytics
      • Data Science
      • Digital Marketing
    • Computer Science
      • Artificial Intelligence
      • Cloud Computing
      • Computer Coding
      • Cybersecurity
      • Information Technology
    • Design
      • Instructional Design
      • UX Design
    • Engineering
      • Engineering
      • Industrial Engineering
      • Project Management PMP
      • Systems Engineering
    • Fintech
      • Blockchain
      • Cryptocurrency
      • Fintech
      • Procurement
  • Bootcamps
    • Analytics
      • Business Analytics
      • Data Analytics
      • Data Science
      • Digital Marketing
    • Computer Science
      • Artificial Intelligence
      • Coding
      • Front-End Development
      • Full-Stack Development
      • Information Technology
      • Machine Learning
      • Software Development
    • Design
      • UX Design
    • Engineering
      • Project Management
      • Software Engineering
    • Fintech
      • Blockchain
      • Cryptocurrency
      • Fintech
  • Resources
    • Courses
      • Blockchain
      • Coding
      • Computer Science
      • Data Analytics
      • Data Science
      • Digital Marketing
      • Fintech
      • Project Management (PMP)
      • UX Design
    • Jobs
      • Business Analyst
      • Computer Programming
      • Data Analytics
      • Data Science and Data Scientist
      • Electrical Engineer
      • Graphic Designer
      • Instructional Designer
      • Mechanical Engineering
      • Web Developer
    • Guides
      • Tech Career Mini Course
      • A Career with Numbers
      • K-12 STEM Resources
      • Internships in Tech
      • Best Tech Scholarships
      • A Veteran’s Guide to a Job in Tech
      • Women in Tech
  • Podcast
Home   >   Careers   >   Machine Learning Engineer

How to Become a Machine Learning Engineer

Jennifer Sheriff – Last updated: September 9, 2024

WRITER

Jennifer considers herself a lifelong learner with a growth mindset and an innate curiosity.

On This Page
  • Machine Learning Engineer
  • Machine Engineer Degrees
  • Become A Machine Engineer
  • What Machine Engineers Do
  • Careers And Salary
  • FAQs
  • Resources

A machine learning engineer helps research, build, and design automatic artificial intelligence systems to apply predictive models.

The field of machine learning engineering is only growing as companies look for more talent capable of creating automated artificial intelligence systems. This makes it an ideal career path for those with specialized interests and skills.

In this guide, we’ll take a deeper dive into the role of a machine learning engineer, including a look at the recommended skills background, and steps needed to become a machine learning engineer.

What is a Machine Learning Engineer?

Machine learning engineers apply the principles of mathematics and computer science to design and build AI systems based on predictive modeling. Predictive modeling is a mathematical process used to predict future events by analyzing patterns in input data.

There are many machine learning and automated artificial intelligence applications already in use, especially when it comes to digital media, communication, and technology. A common application of machine learning is facial recognition in social media images.

Social media platforms like Facebook and Instagram can predict which users the account holder may want to tag based on the people in the image. A second popular application of machine learning is speech recognition.

Machine learning can translate speech into text, and through a user’s guidance, software applications can convert live voice and recorded speech into text files with greater accuracy. The most famous speech recognition products are household devices like Google Home and Amazon Alexa.

The machine learning engineer’s role goes beyond basic computer programming and requires the creation of programs that will enable machines and software to perform tasks without being directed by humans.

In general, a machine learning engineer acts as a bridge between the statistical and model-building work and the building of robust machine learning and artificial intelligence systems, platforms, and services.

As a real-world example, machine learning engineers can help financial professionals predict whether a transaction is fraudulent or legitimate.

Predictive modeling can allow machine learning to classify input data into multiple groups, which rules set by the analyst then define.

After the software organizes the data into stratified groups, analysts can calculate the probability of an error. Engineers work to improve prediction systems to calculate the possibility that an error has occurred, making everyday life easier for consumers and company leaders.

Within this role, machine learning engineers are responsible for implementing isolated statistical analysis, machine learning, and artificial intelligence data into high-performance, accessible systems that provide quick and easy access to end-users. Only a small portion of the machine learning engineer’s job is writing the actual machine learning code.

The rest of the project includes configuration, data collection, data verification, feature extraction, analysis tools, process management tools, machine resource management, serving infrastructure, and monitoring, which support structures necessary to run the machine learning code itself.

What Machine Learning Degrees Are Needed?

Machine learning engineers are expected to have at least a master’s degree and occasionally a Ph.D. in artificial intelligence, machine learning, or data analytics. Advanced knowledge in mathematics and data analysis is critical in a machine learning engineer’s background in the digital age.

Many schools provide students with a master’s degree in machine learning, including Carnegie Mellon University, Cornell University, Georgia Institute of Technology, Duke University, Massachusetts Institute of Technology, Boston University, University of Rochester, UCSD, Stevens Institute of Technology, and Stanford University.

Many programs combine machine learning with another emphasis, such as data science or analytics, such as the programs at Duke, Rochester, and MIT. Regardless of the program, most master’s level degrees allow students to get hands-on experience with computer science, artificial intelligence, and data analytics, which are foundational concepts to a machine learning career.

In addition to degrees, there are also bootcamps and certifications available for people with related backgrounds and experience.

Learn more about bootcamps

How to Become a Machine Learning Engineer

The first step towards becoming a machine learning engineer is building up the required knowledge and experience.  Employers typically look for two things here:

Education

Many job descriptions for machine learning engineers require applications to hold a master’s degree or higher in computer science specializing in machine learning, data analytics, or artificial intelligence.

One reason for this is that machine learning engineers are expected to apply computer science theory to a higher level than expected of bachelor’s degree recipients. A master’s degree demonstrates that an applicant has a familiarity with advanced theory and the necessary skills in coding and project management.

Although a Ph.D. shows further interest in this specialized subject, most machine learning engineers on Kaggle (an online community for machine learning and data science) have disclosed the master’s degree as their highest level of education.

Learn more about machine learning master’s degree

Experience

Many employers want to see some evidence of experience with software development in addition to specialized experience with machine learning and artificial intelligence systems.

Most respondents have between 3 and 10 years of experience, skewing young (between 25 and 35 years old). Internships or participation in events like bootcamps or hackathons can also be used to demonstrate experience.

Although there is an increasing number of machine learning job candidates learning prerequisite information on their own, a master’s degree from an accredited institution is typically expected.

Learn more about internships

Machine learning engineers also need to have the ability to both design and create artificial intelligence applications, which requires knowledge of:

  • Programming Languages: Machine learning engineers are developers and are expected to know how to code at an advanced level.  Although the necessary language(s) to learn depends on an applicant’s desired role, the five best languages for machine learning are Python, R, Java, Julia, and LISP.
  • Machine Learning Model Training Tools: The top tools used for machine learning model training include TensorFlow, PyTorch, Scikit-learn, Catalyst, XGBoost, LightGBM, and CatBoost. Machine learning engineering candidates should be proficient in at least one of these tools, preferably the one listed in the job advertisement.
  • Development and Design Methodologies: Machine learning engineers are expected to design and architect automated applications based on predictive modeling. Machine learning can be simplified into seven major steps: collecting data, preparing the data, choosing a model, training the model, evaluating the model, tuning parameters, and making predictions.

The top-rated companies hiring machine learning engineers in the United States are:

  • Bayer
  • NYC Data Science Academy
  • General Assembly
  • Ford Motor Company
  • HP

Bayer, IBM, General Assembly, Ford Motor Company, and Kmart are known for having the highest compensation packages and the best work-life balance, according to public surveys on Indeed.com.

It can be challenging for candidates to prove that they have the required experience and skills during an interview.  To help them gauge a candidate’s experience, employers typically look for:

  • Portfolio: Employers are increasingly looking for samples of an applicant’s work during the interview process.  Having a Github repository with some past automated ML/AI work can help you get a job as a machine learning engineer.
  • Certifications: Like many technology roles, machine learning engineers are expected to engage in continuing professional education.  Earning certifications and demonstrating skills with relevant platforms, languages, and design methodologies can help meet these needs when developing new skill sets.
Learn more about certifications

Hard skills are not enough to be successful as a machine learning engineer. Machine learning engineers also need certain soft skills, such as:

  • Organization: Machine learning engineers are frequently responsible for bridging data analysts to end-users through clean code and complex automated systems. Doing so effectively and correctly requires machine learning engineers to be organized and detail-oriented, balancing the knowledge and needs of multiple parties.
  • Communications: Machine learning engineers need to be able to work together with data analysts, software engineers, developers, customers, and project stakeholders. A machine learning engineer must have strong communication, collaboration, and project management skills.

What Does a Machine Learning Engineer Do?

At the core, the job of a machine learning engineer is to create automated artificial intelligence systems. However, this can include a few different responsibilities, such as:

  • Translation: Machine learning engineers may be responsible for translating the work of data scientists from environments such as Python and R into other applications that are more accessible to the end user.
  • Web services: Machine learning engineers often create web services and Application Programming Interfaces (APIs) to serve end-users with machine learning and artificial intelligence model results.
  • Automation: Machine learning engineers automate model training and evaluation systems so that the artificial intelligence software can think on its own.
  • Cleaning data: Data used for AI model training must first be cleaned to be readily available for the flow of data between machine learning models and an organization’s data systems.
  • Improving systems: Machine learning engineers are responsible for developing and applying algorithms to improve systems by automating routine tasks that humans would otherwise do.

Not all machine learning engineers perform all of these roles.

A job’s responsibilities often depend on the organization. 

For example, a company may want its machine learning engineers to focus on “big picture” design while tasking a team of developers with implementing this vision.

On the other hand, a small organization may have a machine learning engineer involved in every step of this process.

Machine Learning Engineer Career Outlook and Salary

Machine learning engineering is a career path that is always in demand. As organizations become increasingly reliant on computers as part of their daily business, they need people to design, build, and maintain their software.

The Bureau of Labor Statistics (BLS) predicts a 23 percent growth in the field between 2022 and 2032, which is “much faster than average.”

The BLS does not specifically track machine learning engineers, but it does have information on computers and information research scientists.

A machine learning engineer’s profile is comparable to a computer and information research scientist’s. Regardless of title, each requires a master’s degree or higher in computer science or a related field to design innovative uses for new computing technology.

According to the BLS, the median pay for computer and information research scientists in 2022 was $136,620 per year or $65.69 per hour.

This is generally with a master’s degree and the median years of work experience required by current job listings, so candidates with a higher degree or greater experience can likely expect higher salaries.

Frequently Asked Questions

What is a machine learning engineer?

A Machine Learning Engineer applies mathematics and computer science principles to design and build AI systems based on predictive modeling. Their role is crucial in creating programs that enable machines and software to perform tasks autonomously​.

What educational qualifications are required to become a machine learning engineer?

To become a Machine Learning Engineer, one typically needs a master’s degree or occasionally a Ph.D. in artificial intelligence, machine learning, or data analytics. This advanced knowledge in mathematics and data analysis is essential in the digital age.

What experience is needed for a career in machine learning engineering?

Employers usually look for evidence of experience with software development, along with specialized experience in machine learning and AI systems. Most Machine Learning Engineers have 3 to 10 years of experience and a background that includes internships, bootcamps, or hackathons.

What soft skills are important for machine learning engineers?

Machine Learning Engineers need strong organization and communication skills to effectively bridge the gap between data analysts and end-users, and to collaborate with various stakeholders​.

What are the primary responsibilities of a machine learning engineer?

Responsibilities include translating data science work into user-friendly applications, creating web services and APIs, automating model training and evaluation, cleaning data, and improving systems through algorithm development​.

Related Resources

  • Machine Learning Master’s Degree Programs
  • Find Machine Learning Bootcamps
  • How to Become an Artificial Intelligence Engineer
  • Podcast interview with Syed Rehan
  • Online Master’s in Artificial Intelligence Master’s Degree Programs

Primary Sidebar

Jennifer Sheriff – Last updated: September 9, 2024

WRITER

Jennifer considers herself a lifelong learner with a growth mindset and an innate curiosity.

ON THIS PAGE

  • Machine Learning Engineer
  • Machine Engineer Degrees
  • Become A Machine Engineer
  • What Machine Engineers Do
  • Careers And Salary
  • FAQs
  • Resources

Follow us

About Us | Privacy Policy | Terms of Use | Copyright © 2025 | TechGuide | All Rights Reserved