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. 

Cut This Out of Your Interview Process

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)

• Duplicate questions, worded slightly differently, makes it seem like the hiring manager doesn’t know what the question is asking. It could also feel like a trick, and no candidate wants to feel tricked.

2. Stop using different factors for each candidate

• You need to approach this type of interview very technically, as you are assessing for a technical specialist. To truthfully compare applicants, all outside factors need to be the same. Using different applications and software in an interview can be a significant independent variable that might throw off your results. These are a few that need to be the same 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

• To truly tell if the candidate is highly qualified or not, they need to be put to the test. Instead of asking generalized questions, it would be more beneficial to you and the candidate to give real-world scenarios of things your machine learning team deals with daily. 

4. Lose the ambiguity

• Candidates need transparency, otherwise they will get cold feet. Be upfront about the benefits and compensation your company offers for this position.

Create The Best Machine Learning Engineer Skills Assessment

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. 

Define The Perfect Candidate

One of the best ways to evaluate whether a candidate fit is by creating the ideal candidate beforehand. 

1. Get out a pen and paper or open your notes on your laptop and start by writing out the job description.
2. After getting the description down, get down all the experience that the ideal candidate should have. Is it a master's degree? Ten years in the industry? 
3. Start listing out skills in a candidate by order of importance. Is there a need for a leader on a team? Does the team need a bigger idea person or someone who follows through on ideas already put in place? Are social skills as important in this role as critical thinking? These are the types of skills you need to compare. Determine what would make the best candidate for the position and team.
4. After getting down all the necessary skills, write down what a candidate should not have. After all, the ‘can’t-haves’ are just as important as the ‘must-haves.’ 

Defining the ideal candidate will give you a great outline of what you are looking for in the screening process.

Determine Who, What, and Where

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. 

Video Answers

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:

• Critical Thinking
• Problem-Solving
• Organization
• Communication

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. 

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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. 

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