A relevant higher degree
At least in the Netherlands, most of those applying to work as machine learning engineers already have a masters degree under their belt, which are usually in very specific domains, such as AI, data science, and econometrics. Individuals with data engineering skills (which are taught in some computer science programs) are particularly appealing to recruiters too, as they are more likely to be able to properly structure data pipelines.
“As recruiters, we’re facing a shortage in data engineering talents because very few university programs teach data engineer skills such as deploying a model in the cloud, which are things that are usually learned on the job,” explains our Campus Recruiter Daniela Alvaran.
“Despite this trend, there still seems to be a particular interest in graduates who come from backgrounds such as computer science, as they are more likely to be able to productionalize and deploy APIs”, adds Daniela.
Ability to work with data, models and clouds
More specifically, an MLEs qualifications must include familiarity with programming in Python, and the ability to write code, implement models, and work with data. Moreover, being familiar with cloud computing and architecture is becoming increasingly important, as the number of companies moving their work to the cloud continues to grow.
Our MLEs unanimously agree that communication skills are crucial for a successful machine learning engineer.
“It is really important to be able to translate between the technical part and your clients’ needs, because if you cannot check with your client that the product you’re making is something that they need, can use or understand, you will have wasted everybody’s time and money,” explains Hella.
“As an MLE, you deal with a wider variety of stakeholders, such as business stakeholders, data scientists who created the model, and data engineers who need to incorporate it into a data pipeline. You need to be comfortable with communicating your MLE knowledge clearly,” adds Josko.
Pelle adds being social to the qualities of a successful MLE. “Be open to learning from others and asking questions, because no one can know everything just by Googling it.” Xomnia’s Machine Learning Development Program offers a unique opportunity in this regard, because it puts its members within a network of experienced data professionals.
“When you’re part of our MLE Development Program, you can ask questions, get up to date with the latest state-of-the-art innovations that change every day, engage in peer-to-peer learning and get each other excited,” says Daniela.