Understanding & Hiring Machine Learning Engineers

What is a Machine Learning Engineer?

Machine learning engineers focus on building predictive models and machine learning systems.

What skills are required to be a Machine Learning Engineer?

Machine learning engineers need to understand the common techniques and algorithms used in the field, and what their strengths and limitations are. Depending on the role, an advanced academic knowledge of the way algorithms work and underlying mathematics may be required, or a PhD in the field. In other cases, having practical experience in applying the various techniques is what's most important. Python is the language most commonly used to interact with popular Machine Learning toolkits, but work on the underlying libraries themselves is done in low level languages like C or C++. Some of the skills of a Data Scientist are also important, like interacting with the platforms where data is stored, and generating analyses and charts.

Machine Learning Developer/Engineer archetypes

Infrastructure-Focused Machine Learning Engineer

Archetype

This is a machine learning engineer who focuses on building scalable and high-quality machine learning systems. In addition to their ML knowledge, they are also strong back-end engineers who are comfortable working with large distributed systems.

Modeling-Focused Machine Learning Engineer

Archetype

This is a machine learning engineer who focuses on developing models. They are comfortable translating academic ML into product innovations, and tend to focus on rapid prototyping.

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