A machine learning engineer (ML engineer) is a person in IT who focuses on researching, building and designing self-running artificial intelligence (AI) systems to automate predictive models. Machine learning engineers design and create the AI algorithms capable of learning and making predictions that define machine learning (ML).
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An ML engineer typically works as part of a larger data science team and will communicate with data scientists, administrators, data analysts, data engineers and data architects. They may also communicate with people outside of their teams, such as with IT, software development, and sales or web development teams, depending on the organization's size.

ML engineers act as a bridge between data scientists who focus on statistical and model-building work and the construction of machine learning and AI systems.

The machine learning engineer role needs to assess, analyze and organize large amounts of data, while also executing tests and optimizing machine learning models and algorithms.

Roles and responsibilities of a machine learning engineer

An ML engineer's primary goals are the creation of machine learning models and retraining systems when needed. Responsibilities vary, depending on the organization, but some common responsibilities for this role include:

  • Designing ML systems.
  • Researching and implementing ML algorithms and tools.
  • Selecting appropriate data sets.
  • Picking appropriate data representation methods.
  • Identifying differences in data distribution that affects model performance.
  • Verifying data quality.
  • Transforming and converting data science prototypes.
  • Performing statistical analysis.
  • Running machine learning tests.
  • Using results to improve models.
  • Training and retraining systems when needed.
  • Extending machine learning libraries.
  • Developing machine learning apps according to client requirements.

Skills and qualifications to become a machine learning engineer

To become a machine learning engineer, an individual should have experience with these skills and qualifications:

  • Advanced math and statistics skills, surrounding subjects such as linear algebra, calculus and Bayesian statistics.
  • Advanced degree in computer science, math, statistics or a related degree.
  • Master's degree in machine learning, neural networks, deep learning or related fields.
  • Strong analytical, problem-solving and teamwork skills.
  • Software engineering skills.
  • Experience in data science.
  • Coding and programming languages, including Python, Java, C++, C, R and JavaScript.
  • Experience in working with ML frameworks.
  • Experience working with ML libraries and packages.
  • Understand data structures, data modeling and software architecture.
  • Knowledge in computer architecture.