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Machine Learning Engineer vs Researcher

Artificial Intelligence is an exciting field to be a part of right now, and Machine Learning professionals are in high demand. There is a significant division among Machine Learning professionals, particularly between those who do research and those who design solutions.

The purpose of this essay is to highlight some critical differences between the roles of a Machine Learning Researcher and a Machine Learning Engineer. By emphasizing the distinctions between the two roles, you may equip yourself with the knowledge necessary to make more informed academic and professional decisions.

Machine Learning Researchers

Researchers in machine learning are those who study data and develop machine learning algorithms. They cleanse and analyze data and then construct models using a mix of machine learning techniques and historical data.

Machine Learning researchers are often academics with experience in university research initiatives. Frequently, researchers need extensive software infrastructure assistance. This is mostly DevOps operations, including developing data pipelines, automating testing, provisioning computing clusters, and guaranteeing model reliability. This is entirely new territory for the majority of scholars. As a result, a new kind of job known as the machine learning engineer has emerged.

There is a significant technical barrier separating research and production systems in the physical world. Reproducibility, records management, validation, and cooperation all play a considerably lesser role in theoretical or research contexts than in production systems, where researchers may collaborate on the same data and models to improve them.

To put it simply, academic researchers often work alone or in small groups. Researchers start with a predefined dataset and train a model on it; once happy with the findings, they publish a paper and seldom revisit the code or implement the model for real-world applications.

Machine Learning Engineers

Engineers that specialize in machine learning are the researchers’ backbone. Machine learning engineers seldom interact with the models or are concerned with the format or quality of their work data. Their primary objective is to make the researcher’s life as simple as possible.

Machine learning engineers are responsible for everything from setting up and managing data lakes to creating easy-to-use computing clusters for training and ultimately guaranteeing high-availability model deployment. Machine learning engineers often come from software development and DevOps and have begun to specialize in ML infrastructure.

These individuals are conversant with containers, container orchestration – technologies such as Docker and Kubernetes –, cluster administration in various compute clouds or on-premises, and the development of solid deployment pipelines.


Machine Learning Engineers work in artificial intelligence, developing programs and algorithms that allow machines to operate autonomously. A self-driving vehicle is one of the finest examples of what a machine learning engineer can do via coding and the application of appropriate algorithms. Their primary function is to let computers learn naturally and improve via experience without being programmed.

On the other side, Machine Learning researchers research and develop algorithms utilized in adaptive systems in artificial intelligence. These scientists develop techniques for anticipating product suggestions or recommendations and product demand or forecasts, and they analyze vast amounts of data to extract correlations.

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