Hire a Machine Learning Developer [How To]

Though not always necessary, it’s a plus when you find someone who has worked on a similar project.

If you find a promising candidate who does not have prior experience with a project like yours, that’s not necessarily a deal breaker. You just want to be sure they understand the objectives and have the skills and understanding required.

Structure The Interview

All interviews — regardless of the position you’re hiring for — should be structured. Here are some tips on creating an air-tight interview process to ensure the most qualified candidate lands the role.

Record The Interview

Be sure to ask for permission to record. A recorded transcript can be really helpful to review afterward, as details can be missed in real time.

Inquire About Project Process

Ask each interviewee about the methodology they use to approach ML problems. Have a few scenarios prepared and ask them how they would solve each one.

Ask For Specificity

Request that each candidate share their experience with data cleaning, feature engineering, and model deployment, providing examples whenever possible.

Ask that they explain their previous projects in detail, including the challenges and solutions they devised to mitigate the difficulty.

Instruct them to share their familiarity with various machine learning operations and production environments.

Testing their knowledge of MLOps is important; MLOps are well-established collaborative practices for communicating and working with data scientists and other operations professionals.

Staying abreast of MLOps best practices simplifies the management process, ensures higher-quality output, and helps automate the deployment of machine learning and deep learning models.

Ask Revealing, Industry-Related Questions

Even without deep ML knowledge, you can (and should!) still ask probing, industry-specific questions to ascertain how knowledgeable and experienced the candidates you interview are.

Here are a few examples of questions you may want to ask:

• “How would you approach (list a specific problem) from data collection to deployment?”
• “How would you evaluate success for (list specific case)? What metrics would you use?”
• “In the past, when a model didn’t perform as expected, how did you diagnose and/or fix it?”
• “What’s your strategy for balancing model complexity with business objectives?”
• “How do you explain model decisions to stakeholders who lack in-depth technical understanding?
• “What is your experience with agile?”
• “What experience do you have with big data? What challenges did you face, and how did you overcome them?”

Get Assistance

Consider bringing in a machine learning expert, such as a technical consultant specializing in ML, to assist with the interview and assessment process.

Conduct Assessments

Use technical assessment platforms that have in-depth, ML-specific challenges. Make sure that the test is specific to the role and covers the knowledge required for success.

Whenever possible, try not to put time constraints on the assessments. This can cause unnecessary pressure that makes some people nervous and affects their performance.

Request References From Previous Employers

Ask for references from past employers that specifically relate to what the candidate did as an ML developer. Ask for any examples of the candidate’s work or experience they would be willing to provide.

Watch For Red Flags

As always, it’s important to be mindful of red flags, such as:

Algorithm Overemphasis

Watch out for an overemphasis on algorithms and less discussion about data quality or business context, both of which are equally important and should not be overlooked.

Lack Of Experience

Candidates who have limited experience with deployed models in production environments may lack the knowledge and competency necessary to get the job done.

Lack Of Continuous Monitoring

Candidates who are unfamiliar with machine learning monitoring and maintenance practices are likely too inexperienced to accomplish the task at hand.

No Collaboration Experience

Another potential red flag is a lack of experience collaborating with stakeholders. It’s imperative that whoever you hire is able to grasp the task at hand and communicate with others involved in different capacities.

Create A Practical Test

As well as a comprehensive test, subject candidates to a practical assessment. This could be a take-home test, or one they do on site.

Use actual company data. The test should require candidates to explain their work, including technical choices and trade-offs.

Candidates should present the findings to stakeholders. If an in-person presentation is not feasible, have the candidates generate a video explanation.

Conclusion

By breaking the process down and getting intentional about who you’re looking for and why, you’re much more likely to find a highly skilled professional who knows how to implement the development services you need.