Machine Learning Engineers


Education and Training Requirements

High School

In high school, take as many computer science courses as possible, including computer science, programming, data security, and software development. Math classes—including algebra, linear algebra, trigonometry, calculus, discrete mathematics, applied mathematics, and statistics—will also be useful because mathematics comprises the building blocks of AI and machine learning. If you plan to launch your own company, take classes in business, accounting, and marketing.

Postsecondary Education

Only a few colleges and universities offer degree programs in artificial intelligence (and none yet have launched specialized programs in machine learning). Carnegie Mellon University in Pittsburgh, Pennsylvania, became the first U.S. school to offer an undergraduate degree in AI in 2018. In addition to classes in computer science, science and engineering, ethics math and statistics, humanities and the arts, and AI electives (in decision making and robotics, machine learning, perception and language, and human and AI interaction clusters), students must take the following core courses in AI:

  • Concepts in Artificial Intelligence
  • Introduction to Artificial Intelligence Representation and Problem Solving
  • Introduction to Machine Learning
  • Introduction to Natural Language Processing OR Introduction to Computer Vision

Other U.S. schools that offer AI-related degrees include Indiana University at Bloomington (bachelor of science in intelligent systems engineering) and Northeastern University (master of professional studies in enterprise intelligence). The University of Limerick in Ireland offers a master of artificial intelligence, and the University of Edinburgh in the United Kingdom offers a bachelor of science in AI.

You do not need a degree in AI to work as a machine learning engineer. Many MLEs have a bachelor’s degree in software design, computer programming, computer science, statistics, software engineering, data science, mathematics, or a related field. Many students combine study in one of the aforementioned fields with a minor, specialization, or certificate in machine learning, machine intelligence, AI, or a related field. Some companies require applicants to have master’s and even PhDs in one of these fields.


A growing number of colleges and universities offer certificate programs in machine learning, artificial intelligence, cognitive science, machine intelligence, and related fields. For example, Cornell University offers a certificate in machine learning to students who complete the following classes:

  • Problem-Solving with Machine Learning
  • Estimating Probability Distributions
  • Learning with Linear Classifiers
  • Decision Trees and Model Selection
  • Debugging and Improving Machine Learning Models
  • Learning with Kernel Machines
  • Deep Learning and Neural Networks.

Earning a certificate is a good way to expand your knowledge and explore a field without having to invest in the cost of a degree. Additionally, current engineers should earn certificates to expand their skills and improve their chances of being promoted and earning higher pay. Contact schools in your area to learn about available programs.

Other Education or Training

Machine learning is a fast-changing field, so it’s extremely important that aspiring and current MLEs learn as much as they can about emerging specialty areas, new programming languages, and other topics that will help them land their first job or get promoted. Continuing education opportunities are offered by professional associations such as the Association for Advancing Automation (AAA), Association for the Advancement of Artificial Intelligence (AAAI), and the IEEE Computational Intelligence Society, as well as tech companies and online learning platforms such as Coursera, edX, Udacity, and Khan Academy. For example, the AAAI offers a Symposium on Educational Advances in Artificial Intelligence at its Conference on Artificial Intelligence. The AAA offers webinars such as The Intersection of AI, Collaborative Robots and Machine Vision; Deep Learning and 3D Vision in Identification; and Are We There Yet? The Collaboration Between Robots and Vision. Recent classes offered by include Launching Into Machine Learning, Fundamentals of Machine Learning in Finance, and Introduction to TensorFlow for Artificial Intelligence. Udacity offers Intro to Artificial Intelligence, a free online class that takes about four months to complete. It also offers nanodegree programs in a variety of areas, including Intro to Machine Learning with TensorFlow, Machine Learning Engineer, and Artificial Intelligence.

Certification, Licensing, and Special Requirements

Certification or Licensing

A variety of certification credentials are available for MLEs. For example, the Artificial Intelligence Board of America offers the artificial intelligence engineer credential to applicants who meet educational and work experience requirements and pass an examination. Visit for more information. Software development firms also provide certification programs. For example, SAS offers the AI and machine learning professional credential to applicants who complete five courses and pass several examinations.

Since the collection and analysis of large amounts of data plays a major role in their work, many AI specialists earn data-focused certifications. Certification credentials are offered by the Institute for Certification of Computing Professionals (certified Big Data professional, business data management professional, and certified data scientist), DAMA International (certified data management professional), INFORMS (associate certified analytics professional, certified analytics professional), and TDWI (certified business intelligence professional). Contact these organizations for more information.

Experience, Skills, and Personality Traits

The ideal machine learning engineer has several years of experience using ML in applied research settings, but you can enter this career straight out of college. Entry-level workers must have completed an internship, cooperative education, or fellowship opportunity in machine learning or artificial intelligence.

Machine learning engineers need a variety of technical skills to be successful in their careers Although skill requirements vary by employer and job title, here are some in-demand technical skills according to the recruiting firm Robert Half Technology and other sources:

  • knowledge of programming and software design fundamentals
  • knowledge of basic algorithms and object-oriented and functional design principles
  • extensive data modeling and data architecture skills
  • programming experience in Python, R, and Java (the top languages for MLEs), as well as in C++, C, JavaScript, Scala, and Julia
  • background in machine learning frameworks such as TensorFlow or Keras
  • knowledge of Hadoop or other distributed computing systems
  • experience working in an Agile environment
  • advanced math skills (linear algebra, Bayesian statistics, group theory)
  • experience with research and development protocols
  • experience in probability and statistics modeling procedures
  • the ability to perform graphics processing unit programming

Each MLE position has different skill requirements, so it’s important to review job advertisements to learn about specific software and other technical requirements to learn what’s required for your target career or industry.

Key traits for machine learning engineers include excellent oral and written communication skills (including the ability to explain complex processes to IT novices); high levels of concentration; a detail-oriented personality; strong analytical and time-management skills; creativity and an innovative mindset; a passion for the field of machine learning; and a willingness to continue to learn throughout their careers.