Machine Learning engineers are increasingly sought after, so harder to find. This is how to find and recruit Machine Learning engineers anyway.
Machine Learning is an increasingly relevant domain within almost any industry, especially in tech driven businesses.
The statistics show that 3.92% of the IT jobs in the end of 2021 and beginning 2022 were related to Machine Learning. This figure has been growing at almost 10% in the last year. And Machine Learning engineers get offered an average annual salary of $93.000 (IT Jobs Watch, UK, 2022). In the United States the reported average yearly salary of a Machine Learning engineer even goes up to $120.000 (Glassdoor, 2021).
Moving between hype and
Gartner places Machine Learning between the peak of inflated expectations and trough of disillusionment, meaning that Machine Learning is currently still a hype but people also look at it increasingly more critically.
So what do we make of this when you’re looking to hire Machine Learning experts?
In this blog we’ll give some guidance on:
Machine Learning engineers are typically strong software programmers who also have a good understanding of data models and more specifically learning models.
They research, build and test learning algorithms that they deploy in digital products that are self learning or within companies that use those models to improve processes.
Machine Learning experts typically work on the crossroads between data science, engineering and DevOps related activities.
Some of the most important activities of a Machine Learning engineer:
Most important skills of a Machine Learning engineer:
Machine Learning engineers can be found on a variety of platforms. Most recruiters will limit themselves to LinkedIn, but most Machine Learning engineers can be found on other platforms.
Platforms like Kaggle, Stack Overflow and GitHub are full of Machine Learning engineers who are sharing code and databases with each other which generates valuable information about their actual skills and interests.
Knowing how to source these platforms will give you a competitive edge as a recruiter, since not a lot of recruiters utilize these sources.
Here’s an overview of the best platforms to find Machine Learning talent:
Kaggle is an online community for data scientists and machine learning experts. ‘Kagglers’ participate in data science and Machine Learning challenges and share data sets, collaborate on code and solve problems together.
There are 5 million data scientists and machine learning experts on Kaggle.
Stack Overflow is a question and answer website for software engineers. Users can earn reputation points by providing valuable answers to questions that are asked by the community.
With 18 million engineers and rich information on related technology skills, Stack Overflow is a must for sourcing Machine Learning talent.
GitHub is a code repository and version management platform. Engineers deposit their code repositories and collaborate on the code to improve.
GitHub has 65 million engineering users on it, more engineering profiles than on LinkedIn or any other platform.
How do you source from these platforms?
These platforms are not designed for recruiters to find people. They are designed for the users to collaborate. You have to know how to source these platforms or use the right tooling in order to find the right candidates and reach out to them.
The biggest challenges of hiring Machine Learning engineers are that they are high in demand and that it’s hard to understand what makes up a good Machine Learning engineer.
You can set the hiring process up for success by bringing in assessments, having a differentiating offer and deploying a quick hiring process.
Once you’ve found your candidates on Kaggle, GitHub, LinkedIn or Stack Overflow you need a decent understanding of how suitable your candidates are for the job.
In general terms, you need a good understanding of their skills and personality.
Skills assessments are a good way to assess the minimum level of those required skills.
Examples of standard data skills assessments:
Alternatively to a standardized skills assessment, you can give the candidate Machine Learning assignments which they can make to show their ability to build what you need for your company.
To assess personality there are few reliable assessment tools. But you can create a (semi)structured interview with predefined questions targeted towards personality.
Examples of personality questions to include are:
When you need Machine Learning experts you are competing with companies like Google and Microsoft. That is not something that should scare you, but you should be aware of the options that your candidates have.
Have a clear picture on what you can offer what other companies don’t. Are you a startup? Your candidates might be attracted by the idea of the freedom they have to ideate, build models with few limitations and have the opportunity to grow a broad skill set.
Validate the average salary for a Machine Learning engineer at the level that you’re hiring for, and don’t forget to assess that salary based on the country that the future hire will be working and living in.
Consider to offer any other benefits on top off the salary, like employee stock options, extra holidays and working from home.
Machine Learning engineers live for efficiency. So when your hiring process is not efficient and does not facilitate them to get the right information quickly, you’re likely to lose a lot of candidates throughout the process. Also when you’re hiring is slow, it is very likely that your competitors will win the candidate for them instead of you.
Bring speed into your hiring process and set clear expectations for the candidate. Try to bring your time to hire down to anywhere under 6 weeks by responding quickly to candidates, having enough touchpoints with the candidate to check where they stand, and by automating processes like interview scheduling and follow up.
Recruiting is competitive, if you know how to find talent that is less competed for, you win.
LinkedIn is full of surprises, and hacks... This is how to make use of all the free stuff and work arounds in LinkedIn.