Machine learning engineers are a top priority for a lot of industries. These highly skilled professionals never seek employment long and are often offered new opportunities before they need one. That’s why examining these professionals as quickly and efficiently as possible is important for retaining talent. Create the best machine learning engineer skills assessment and follow these next steps.
Before you can develop the best interview process, it’s helpful to know what makes up a poor interview process. Machine learning engineers are trained to create and analyze algorithms. An interview process is just that, one big algorithm. If your ‘algorithm’ is not perfectly organized, these candidates can sniff it out from miles away. That makes the process even more important. Suppose your company is looking to hold a machine learning engineer skills assessment. In that case, it needs to be formalized and impenetrable.
1. Get rid of duplicate questions (worded slightly differently)
2. Stop using different factors for each candidate
I. Test environments for written and video recorded answers
II. Video conferencing with screen sharing features
III. An environment to build real-time diagrams
Along with software and applications, when you need to verify multiple candidates' identities, ask for the same references. Examples of ways to verify someone's identity include:
I. Reference their LinkedIn
II. Reference their GitHub
3. Only assess real-world problems
4. Lose the ambiguity
Like a machine learning engineer; we need to verify that the data we have been collecting is accurate and clear. We can do this by creating a fair assessment of each candidate. There will always be multiple steps in an interview, which is why it is called a process, but we need to ensure each step is necessary. Look at the above section and cut out any unnecessary questions or steps you previously had in your interview process. Next, you need to create a series of steps that not only analyze their hard skills but also analyze their soft skills.
One of the best ways to evaluate whether a candidate fit is by creating the ideal candidate beforehand.
Defining the ideal candidate will give you a great outline of what you are looking for in the screening process.
Of course, multiple people will evaluate the candidate besides the hiring manager. List out the different parts of the interview process and assign them to the respective individuals. The hiring manager might have the first phone call and send out the written test, but the team leader will be next once the screening is complete. They may give a one-on-one video interview where the candidate goes over real-world questions with an IDE or coding environment, and if the candidate passes, they will move to the next round. This scenario is just an example of defining the who, what and where.
The beginning part of the process is just as important in a machine learning engineer skills assessment as the end because this is where you will determine whether the candidate gets the full evaluation or not. In a take-home written test, it’s hard to accurately determine a candidate's strengths and weaknesses. This step is where the use of videos comes into play.
Written answers give you a great understanding of the candidate's answers when given time to piece together words properly and edit them to perfection. Recorded answers give you the raw version of the answer. You can tell much about a person by their body language and confidence. There is less time to think about the answer, the candidate can not edit their words, and it’s also a great way to stop fraud/plagiarism. Recorded answers identify both hard skills and soft skills. While watching the video, you can determine whether the answer is correct.
Some additional points to take into account include:
Ultimately, you can not have the ideal interview process without determining what makes a bad interview and determining what makes a successful one. The hardest part is keeping all the factors the same, so there can be a true machine learning engineer skills assessment. That is why Filtered is here to implement the perfect aspects needed for this process.
Filtered is a leader in skills-based, data-driven recruiting technology. Our end-to-end technical hiring platform enables you to spend time reviewing only the most qualified candidates, putting skills and aptitude at the forefront of your decisions. We’ll help you automate hiring while applying objective, data-driven techniques to consistently and confidently select the right candidates. To get started, contact our team today or register for a FREE demo.