A degree from a good university or a few years at Google can be a useful conversation starter. It doesn’t tell me how someone will handle a vague customer problem, learn an unfamiliar system or get a small product into use.
Those are the things I’d want to understand before hiring an engineer into a small team using AI. Here, I mean a software engineer working with coding agents, rather than a specialist training machine-learning models.
My view is that as agents take on more implementation, the ability to direct work becomes a bigger part of the job. I’d want evidence that a candidate can do that, wherever they studied or worked.
Find out how they learn something difficult.
A list of frameworks tells you what someone has encountered. Ask about a time they had to understand something unfamiliar, with nobody available to teach them.
What made it difficult? Where did they start? How did they discover that their first explanation was wrong? What did they build or change once they understood it?
I’d be interested in someone who can follow a problem beyond their current expertise and work out whether they have understood it properly. That might come from a job, independent study or a project they can discuss. It doesn’t require a public portfolio or a second working week of side projects.
AI makes it easy to get an explanation. The useful skill is knowing how to test that explanation, notice gaps and keep learning when the first answer isn’t enough.
Ask what they have owned from beginning to end.
In a large team, someone can become very capable at one part of a system without having much exposure to the rest. That may be exactly what your role needs. If you need someone to take a customer problem through to a working product, ask for evidence of that broader responsibility.
Who decided what to build? Who spoke to the customer? What happened when the feature reached production? Who dealt with the problems afterwards?
A team lead may have good examples, but the title isn’t the qualification. A developer who maintained a small service could have made more of these decisions than someone with a much more senior title. Let them explain their contribution within the team, including where other people helped.
This starts with being clear about the role you actually need. Don’t select for broad ownership and then give the person a narrow remit with no authority to make decisions.
Look for someone who can let the work go.
When an engineer directs several agents, some of the work resembles technical leadership. They have to explain the goal, separate tasks that can proceed independently, agree interfaces and check that the pieces fit together.
I’d look for someone comfortable giving an agent a bounded task and enough space to attempt it. They should know what evidence they need before accepting the result and when to intervene.
That requires technical understanding. If an agent adds a queue, changes a permission boundary or rewrites a database query, someone still needs to judge whether that choice fits the application.
“What would you let an agent do without checking each step, and what would make you stop it?”
Listen for an answer tied to the particular task: the consequences of a mistake, the checks available and how easily a change can be reversed. Unlimited trust and constant intervention both need questioning.
Ask where their standards save time.
I’d be cautious about someone who feels they must personally shape every function before anything can ship. If all the work has to pass through their preferred implementation, adding more agents may just create a longer queue for their attention.
That concern isn’t a reason to dismiss people who care about clean code. Good structure, useful tests and clear boundaries can make delegation possible. The question is whether they can turn their standards into guardrails the agent can follow and checks it can run.
Ask about a change they accepted even though they would have written it differently. What made it good enough? Then ask about a change they refused to ship. What was at stake?
A label such as “perfectionist” tells you little on its own. Concrete choices help you see whether the person can spend their attention where it matters.
Make room for someone to change their mind.
I want to hear about a technique that stopped working, a tool they reconsidered or a decision they undid. What evidence made them change course? Did the second approach help?
Keeping up with AI involves trying things, finding limits and occasionally abandoning a method that used to work. Knowing every new tool’s name is less useful than being able to explain when a tool helped and when it got in the way.
Our interview questions and follow-ups include ways to explore those decisions without turning the conversation into a test of confidence.
Give them a chance to demonstrate it.
For the kind of role described here, I’d use a small, relevant codebase, competing requests and a clear time limit. Let candidates use AI, explain what you will assess and ask them to leave a short account of their decisions.
Then explore what they clarified, delegated, verified and left unfinished. You can see whether their account holds up against the work. The full-stack assessment guide explains how to build that exercise.
This is the profile I’d investigate for a small team with broad responsibilities. A specialist role may need something different. Start with the work, and give candidates from different backgrounds the same chance to show they can do it.
Research and further reading
This is a hiring argument to test against your role, not a validated claim that a particular background or personality predicts success with AI.
- OPM: job analysis. Supports connecting selection criteria to the actual tasks and abilities a job requires.
- Anthropic: building effective agents. Describes task decomposition, feedback, checks and human oversight. These engineering practices inform the delegation examples here; the article does not validate a hiring profile.