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Machine Learning Engineer (MLE)

Last updated: June 2, 2021

What Does Machine Learning Engineer (MLE) Mean?

A machine learning engineer (ML engineer) is an information technology (IT) professional who is responsible for helping to build and maintain an organization's machine learning (ML) algorithms and artificial intelligence (AI) systems.

An important goal of an ML engineering's job is to make it easier for data scientists to access and find value from extremely large data sets.

In a large enterprise, ML engineers require background skills that lie somewhere between those required by certified data analysts and data scientists with advanced degrees. Typical job responsibilities include:

  • Pre-processing training data.
    • Tasks includes data collection, normalization and standardization, as well as data pipeline construction, ML model selection and hyperparameter tuning.
  • Working with data science and operations teams to automate predictive models.
  • Continuously monitoring outputs to ensure they remain within defined limits and are as free from machine bias as possible.
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Techopedia Explains Machine Learning Engineer (MLE)

The machine learning engineer’s work requires a strong background in algorithm development and ML design. The MLE has to be conversant in how these technologies work. Beyond that, the MLE has to understand how to work with data (in the case of ML technologies, training and testing data and production data sets), and be able to contribute to the full life cycle of a ML project.

In addition, the MLE often works with diverse stakeholders. The typical MLE is "close to the bare metal" in terms of working directly with the technologies, and by extension, ML teams, but may also present to executives or even peripheral audiences, such as client teams or VC people.

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Computer ScienceEmerging TechnologyMachine LearningData Science

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