Machine Learning Engineers

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.