5 Job Interview Tips from a SurveyMonkey Machine Learning Engineer

Machine learning engineering is an increasingly in-demand profession—as a growing number of companies look to ML algorithms and artificial intelligence to streamline and improve their businesses, more and more opportunities have opened up to those with backgrounds in machine learning, natural language processing, and data science.

But it’s also a highly competitive profession. Most machine learning engineers have backgrounds in software engineering or have built up their machine learning knowledge and expertise through online courses, bootcamps, or hands-on industry experience. This is why, when applying to machine learning engineering roles, it helps to highlight this more than your education or work experience.

Shubhi Jain is a machine learning engineer who has worked at companies like Apple, Inclusive, and SurveyMonkey. Below, he shares some of his top job interview tips for how to stand out from the crowd when applying for a machine learning engineering role.

Tip 1: Diversify your CV

Including machine learning projects you’ve completed in your portfolio is a no-brainer. But it helps to showcase a diverse range of projects, particularly those that other ML engineers might not have encountered. “I would recommend, at least on your resume, listing a lot of different projects that you might tend to do, especially ones that aren’t really commonly performed,” Jain said. “I think a lot of people have tackled, say, the Netflix challenge as a project within their engineering courses, or even as part of their MOOCs. And a lot of people have tackled things like the Titanic project, and these are very commonly known projects.” While it’s important to be familiar with some of these common projects, Jain recommends going beyond the obvious.

Tip 2: Lean into your interests

Where might an aspiring machine learning engineer or data scientist find unique project ideas? Jain recommends leaning into personal interests because not only can they be a goldmine for machine learning projects, they can also allow an ML engineer’s passion to shine through. “For example, I’m really interested in sports,” Jain said. “And so I’ve done a lot of sports analytics projects where I’m really working on scraping data from the troves of data warehouses that are publicly available and trying to make some interesting projects out of that. And that’s what really makes you stand out.”

Tip 3: Know your algorithms

Once you’ve built a strong CV that lands you the initial interview, Jain believes it’s of utmost importance to know your algorithms (same for individuals who wants to work in the data science field ) and be able to clearly explain how models from former projects work. This means knowing the math behind the model, being able to explain how the model itself works, and discussing how the model fits in with the broader project. “I think something that would blow me away probably is if they have a project or something that they can really point to and show me end-to-end how this works, why they chose to do or implement this project in a certain way, the trade-offs that exist there, [and] what the impact of this project really is,” Jain said. “Not in terms of like, Hey, I just got 93% accuracy on this machine learning model, but what was the value that came out of that? Did you help a team of people detect that there’s deforestation going on where they wouldn’t be able to do that in an automated fashion? Things like that, where that really closed the loop on why we’re really doing machine learning.”

Tip 4: Think about the big picture

It’s important to be able to impress recruiters on a technical level, Jain said. But increasingly, hiring managers and executives are also looking for machine learning engineers who can see the bigger picture and understand the role of machine learning within a business or organization. “A lot of people really have the technical stuff down, and that’s amazing,” Jain said. But other things are equally as important, such as “having a vision and recognizing where machine learning is within our organization today; where it can really go in the future; and how do I really work to develop systems that allow us to take that step forward for what may come in the future?”

Tip 5: Show transferability

It’s one thing to be able to speak at length about projects you’ve worked on. It’s another to show hiring managers how your knowledge of machine learning can transfer from one project to the next, and that your foundational ML engineering knowledge will continue to serve you no matter what an organization throws at you. “I’m always interested in what people view as important within machine learning,” Jain said. “Does the ML engineer that I’m looking to have join my team really understand the trade-offs between really high accuracy and really low latency on my models and within the ML systems overall? And [do they understand] why one thing is more important than the other? And I might ask questions specific to situations where, let’s say, I’m going to create a model that detects cancer. What’s important to have in that model? I really love hearing from ML engineers themselves and having [the interview] be more of a conversation about more broad topics, and then being able to drill down to the specific technical details from there.

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This post was written by Tracey Lien.