Machine Learning Engineers

What does a Machine Learning Engineer do?

A Machine Learning Engineer trains a model to either predict or classify data that a normal statistics approach couldn’t handle. For example - let’s say an airline asks for customer feedback on Twitter and receive 10,000+ tweets back. Machine Learning Engineers can train a model to determine whether the feedback was good, neutral or negative. It’s a good way to get customer feedback into data.

What are some of the skills of a successful Machine Learning Engineer?

Machine Learning could also be called statistical learning, because a Machine Learning Engineer will need to have a solid background in statistics. Calculus and linear algebra, as well as understanding how to create derivatives of functions, are also important subjects for a Machine Learning Engineer to be well-versed in. Many Machine Learning Engineers may also have an interest in participating in data competitions like those found on Kaggle, and participation can be a strong indicator they enjoy working in the field and possess self-drive.

What is the typical background of a Machine Learning Engineer?

Many Machine Learning Engineers will have a background in computer science, coding or math from undergrad, or a Masters degree and will have studied Machine Learning itself. Many team leads who are directing the day to day work of Machine Learning Engineers will have advanced degrees, like PhDs, in Machine Learning.

What are some of the languages a typical Machine Learning Engineer may need experience in? 

Python, C++ or C sharp and MatLab and natural language processing can all be important for a Machine Learning Engineer.

What is the difference between Machine Learning and Deep Learning? 

Machine Learning and Deep Learning have both received quite a deal of press over the last several years, so it’s not a bad idea to understand the difference between them when hiring to fill these niche roles. Machine learning has two different types of data sets - unsupervised learning and supervised learning. Unsupervised learning means you have data you don’t know how to label (one example of this could be stocks - you won’t know if a particular stock is profitable or unprofitable). Supervised learning means you have a data set that you know the labels for is for the medical field (one example of this could be you have a data set with confirmed individuals who have diabetes, and you’re looking at different data points that correspond to the person with diabetes). Machine learning works with both unsupervised and supervised data sets to develop models that can make predictions and accurately account for different events. Unlike Machine Learning, in Deep Learning, your data sets won’t have any labels on them, and it will be hard for a human to label the data. Machine Learning Engineers working in Deep Learning will have to train a model to find patterns in unlabeled data, with tens of thousands of data points.

What does a typical day look like for a Machine Learning Engineer?

A typical day for a Machine Learning Engineer could be spent working on their model for a few hours and then meeting with their team to discuss projects. In the afternoon, they may spend some time troubleshooting a potential bug and then working with their team on some data manipulation.

What are some of the typical job titles of a Machine Learning Engineer?

We’ve recruited for many different Machine Learning roles, including:
  • Senior Machine Learning Engineer
  • Senior Machine Learning Engineer - Natural Language Processing
  • Principal Machine Learning Engineer - Natural Language Processing