Looking for your next Machine Learning Engineer hire, or Machine Learning interview questions? We meet with Machine Learning Engineers constantly, and help connect them to top companies and teams. Read on for our tips on finding your next Machine Learning Engineer, what Machine Learning Engineer interview questions to ask, and what to look for during the hiring process.
Machine Learning Engineers are now critical roles in many engineering organizations, and in a variety of industries - from healthcare to advertising to financial services.
Simply put, a Machine Learning Engineer trains a model to predict or classify data that a normal statistics approach couldn’t handle. For example - let’s say an airline asks for customer feedback on Twitter and receives 10,000+ tweets back. Machine Learning Engineers can train a model to determine whether the feedback was good, neutral or negative. This work is essential enough that advanced degrees are now offered in the specific area of Machine Learning.
1. Determine who the role will report into. Will this role report into a Director of Engineering? VP of Engineering? Your company’s Chief Technology Officer? Deciding who the role will work with can help you determine the level at which to advertise. Hiring for an individual contributor is different from a leadership role – from the job description to interview questions.
2. Write out the main project or projects you will want this individual to work on. Will they need to develop a specific machine learning application – for example, to create an algorithm to track and effectively sell to visitors on your website? Will they create new smart product features, or improve upon an existing product?
3. Write out your hiring process and decide if you want to include an assessment. Jot down how many rounds of interviews you want your candidates to complete, which important stakeholders they should meet, and whether a technical assessment will be helpful.
1. Basic coding and software development experience. At a minimum, Machine Learning Engineers should have experience in basic programming languages like Python and web applications like AWS.
2. Specialized knowledge in Machine Learning related technologies. Look for resume keywords like Pytorch, TensorFlow and PySpark.
3. Experience in building frameworks and algorithms themselves. Can your candidate quantify their accomplishments? For example, can they explain how their machine learning model improved search results or led to higher customer satisfaction? Dig into the specifics of their projects.
4. Involvement in projects, competitions, or organizations outside of a full-time role. Data competitions (like those on Kaggle) are popular and participation can be a strong indicator the candidate enjoys working in the field. We’ve also met Machine Learning Engineers who have created neural networks that analyze real estate prices, or have used Google Maps business data from to analyze data and build methodologies. Being involved in peer reviews of source code or displaying projects on Github is a positive indicator of a committed Engineer. Many large data sets are now publicly accessible, and savvy Machine Learning Engineers will know how to use them to pursue projects of interest on the side.
When hiring for a technical role, it’s still important to make soft skills and communication skills part of your evaluation. Remember that this individual will often need to share what they’re working on to stakeholders inside and outside the engineering department – and be able to distill high level information in a comprehensible way.
This is especially critical if you’re hiring a Machine Learning Engineer to join a small team, and will need them to own their entire projects (instead of providing support to a larger team). You will want to make sure your hire can present methodologies, metrics, and dashboards that communicate how the team is pacing toward your goals. Can your hire help identify where you’re meeting or exceeding KPIs, and where your engineering organization needs to improve? Clear communication and presentation skills set great candidates apart.
When putting together interview questions, feel free to pull from our suggestions below. Here are some of the top interview questions we suggest to ask Machine Learning Engineer candidates.
1. Can you tell me about a time when you had to use large data sets to draw insights?
2. What’s an example of an algorithm you’ve created or worked on, and what did you design the algorithm to do?
3. What’s your experience with summarizing, extracting, or diarizing data?
4. What Machine Learning tools have you used, and which are you the most familiar with or expert in?
5. How do you clean or test code?
6. What’s your experience in developing user (or customer) facing systems?
7. What’s your favorite Machine Learning project you’ve worked on? Why was this your favorite?
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