In an era where AI is reshaping engineering workflows, many companies are scrambling to “AI-proof” their hiring process. But is that the right move?
In this episode of the Scaling Tech podcast, we sit down with Anna J McDougall, an award-winning engineering leader, to unpack how technical interviews can evolve with AI, not against it.
Anna shares a refreshing, pragmatic take on what makes interviews fair, realistic, and genuinely predictive of success on the job. From building interview processes that mirror actual engineering tasks to understanding the limits of “cheating” narratives around AI, Anna challenges us to stop relying on outdated measures of technical excellence and start designing for the teams we actually want to build.
Whether you’re a hiring manager or a candidate navigating the new normal, this episode offers real-world insights into what smarter, modern tech hiring looks like.
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Key Insights and Episode Highlights below the video.
About Our Guest:
Name: Anna J McDougall
What she does: Anna is an award-winning engineering leader, TEDx speaker, and author of the Amazon bestselling career guide “You Belong in Tech”. As a Software Engineering Manager at HelloBetter, Anna and her team have successfully implemented these interview methods, cutting hiring timelines while improving candidate quality and team satisfaction.
Where to find Anna: LinkedIn | Website
Key Insights
⚡A great interview process isn’t about finding the “best” individual but about building the right team. To hire effectively, you have to look beyond technical skills and ask: What are we actually missing? Anna puts it this way: “What are we actually looking for when we’re hiring? How do we know what we’re looking for? And how do we assess technical skills then in an age where all of these new tools are coming out but also how do we take that as a factor in when we’re hiring someone and not the factor when we’re hiring someone because I do think you need to look at every candidate holistically and you also need to look at your own teams holistically and even your whole company holistically and think where are our gaps, what are we missing here? If we just go down the road of technical excellence alone, you’re going to end up missing out on a lot of things that could bring innovation that could drive motivation, you know, and other things that help teams stay together and help businesses be successful.”
⚡You can’t AI-proof your team, but you can lead smarter. There’s no universal fix for AI misuse. More rules won’t solve it; better leadership might. As Anna explains, “Create a system and there will always be someone who wants to break it. So I don’t know if you can, and that’s kind of the hard truth about it. You can look out for signs of it. You can look out for signs of underperformance, or people who are never contributing anything in meetings, for example, they’re joining a meeting but their camera and microphone are off the entire time. I mean, once or twice if they have a good reason, that can happen, but if that’s happening a lot, that might be a sign that they’re actually in two meetings. So there are there are signs you can look out for, but it has to really be addressed on an individual basis.”
⚡Great hiring starts with realistic interviews. If your technical interview doesn’t reflect the actual work engineers do, you’re testing for the wrong things. Anna says, “We are thinking about how can the technical interview round mirror the real engineering work that people are doing to the closest level possible. That is the core of it. So, how can we create an environment which is as close as possible to the real work that they will be doing? Because I think that’s the core of it. […] So, how do people work? Well, let’s imagine, what we really want to know is, is this candidate going to perform well when they’re with us at the early stages, for example? So my thinking was, how can we create an environment that is as similar as possible to them solving one of their first tickets?”
⚡ Engineers should be allowed to use AI in tech interviews.
If engineers rely on AI tools every day to code smarter and faster, interviews should reflect that. Anna’s method does just that: a 40-minute pair programming session with a realistic, broken codebase where AI is not only allowed, but encouraged. She explains, “What are we actually testing here? I think that’s important. This is testing delivery. This is testing actually, ‘Can they do it?’. And we tell our candidates very openly, very explicitly, you can use any tool you want here. And that’s where we differ from most people. We say you can use ChatGPT, you can use Google, you can use Cursa, you can use whatever you want, use whatever you would normally use in your work. Don’t try to do something you haven’t used before just because you think we’re expecting it. No, just do what you normally do and show us what you do, that’s fine. We’re not here to test your memory. I know I could never remember the difference between different native JavaScript functions. I was constantly forgetting the names of things. I don’t think that makes me a bad engineer. I just have a terrible memory for the names.”
Episode Highlights
Don’t just hire for skills; hire for what your business needs.
A great interview process reflects what your team actually needs. Every engineer brings a different mix of strengths, and it’s your job to find the right fit, not the loudest voice or fastest coder. As Anna puts it,
“It’s not a one-size-fits-all thing. I’m quite obviously a big old extrovert, but you don’t want a team full of extroverts. That would be horrible. But similarly, when you have a team that’s full of just introverts, you also end up having some problems sometimes in terms of communication things or in terms of cross-functional kind of initiatives. So I guess from that side, I’ve always been interested in the combination of how do teams really work and using that business perspective that I got through my marketing and project management career and kind of bringing those ideas then into tech and saying how do we actually design a system? You can kind of see it like hacking the interview process. How do I actually look at this system and break it down and look at what are actually our goals with building an interview system?”
Bad hires cost more than just money; they cost momentum
When hiring misfires, businesses lose time, confidence, and clarity. The process restarts, fees stack up, and suddenly you’re questioning everything that’s always worked.
As Anna puts it, “The worst thing a business can lose is time and money, and I think you lose both when you make bad hires. You usually have to start the process over again. If there’s a recruiter involved, you have to pay more fees or whatever to find this person again, then you might have the same situation happening again. People start freaking out. You start thinking, ‘What am I going to do? How am I going to do this? I’ve hired in this way for decades. Why is it not working anymore?’ I think that’s part of why I came up with this new system and why I decided to write about it as well, and why I decided to share it with the community because I just kept seeing so much panic over this isn’t working anymore and how can I trust take home tests anymore and how can I loop code when they could have this new tool installed on their computer…I saw a lot of that and I thought, well, you don’t need to worry that much.”
Are you focusing on the wrong thing with AI?
As AI becomes a standard part of engineering workflows, the question isn’t if people will use it, it’s how we prepare them to use it responsibly. Anna explains, “We cannot control someone’s home environment. We cannot control their setup. We cannot control what tools they use. Furthermore, I think there’s a growing awareness that AI is a tool for software engineers. Now, we are talking as software engineering managers, as leaders, as technical leaders, we’re talking a lot about how do we train our people to use this effectively? How can we integrate this into our development tools? How do we make sure that we’re not introducing bad code or insecure code by using these things? How do we get people on board? How do we upskill? We’re having these conversations, and to me, what I saw was a huge mismatch between that conversation and the conversation you and I kind of just had, which is like, ‘Oh, everyone’s cheating, why are they using AI in interviews?’ And are you asking them why they’re using an IDE in an interview? No, because it’s a tool that helps them.”