Machine learning engineers are part of the engineering organization on Quora, while Data Scientists are part of the data science organization at Quora.

At the very highest level, the engineering organization builds the product and the algorithms, making sure that it works reliably, quickly, and at-scale. The data science organization works on understanding Quora's data to inform product and business decisions.

Both machine learning engineers and data scientists are involved in machine learning at Quora. In the context of machine learning, the main summary of the differences is the following:

Machine learning engineers build, implement, and maintain production machine learning systems.
Data scientists conduct research to generate ideas about machine learning projects, and perform analysis to understand the metrics impact of machine learning systems.
Machine Learning Engineers and Data Scientists both have other roles in the company, but for this answer I will solely cover their involvement in machine learning at Quora.

Here is the general breakdown of how each of the roles uses machine learning:

Machine Learning Engineer

Build and implement production machine learning systems (e.g, recommendations, personalized ranking, and a lot more as described in How does Quora use machine learning in 2017?).
Maintain the health of machine learning systems, including speed, reliability, and performance.
Develop internal machine learning frameworks and abstractions to facilitate common tasks such as training / testing, feature use / reuse / creation / storage, and deployment. These abstractions are used by both machine learning engineers and data scientists.
Data Scientists...

Evaluate potential / existing approaches, features, algorithms, and error metrics help improve machine learning systems.
Analyze the impact of machine learning algorithms on key metrics. This involves ad-hoc analysis of A/B tests, and understanding how ML systems fit into top-level metrics of the company.
Research and understand user behavior patterns such as an engagement by building machine learning models. These machine learning models are made for one-off analyses and are not put into production. Their primary goal is to help evaluate ideas.
A machine learning project will often be staffed by both data scientists and engineers. How the collaboration works are best summarized in a three step flow - the three steps in this flow happen continuously and always with the goal of optimizing the development velocity. Here are the steps:

Data scientists conduct research to identify possible needs or improvements in machine learning systems
Machine learning engineers build, implement, or improve the machine learning system
Data scientists evaluate the impact of the machine learning system on company metrics
To learn more about each of the roles (or to apply! We're hiring!), you can check out the respective job postings at https://www.quora.com/careers

Post Credits: Quora Careers Platform to explain the difference between Data Scientist and a Machine Learning Engineer.