Hiring the right Machine Learning Engineer isn’t about ticking off a skills checklist or filtering for textbook-perfect answers. It’s about identifying people who can think through ambiguity, collaborate with your product team, and design systems that actually make it to production.
If you’ve ever left an interview unsure whether a candidate could succeed at your company, you’re not alone.
The reality is, most early-stage founders are trying to interview for a role they’ve never done themselves. And unlike hiring for product or frontend, ML hiring doesn’t have a clean playbook.
That’s why you need a framework.
Not a script. Not a rigid process. But a thoughtful structure that mirrors the way your team solves problems and gives candidates the chance to show how they think, not just what they know.
Here’s what we’ve seen work…
Context-first technical interviews
Too many technical interviews feel like traps. You throw a candidate into a challenge without background, without product context, and expect them to reverse-engineer what you’re looking for. That’s not how real work happens.
Start with clarity.
Begin by walking the candidate through a real problem your team recently tackled. Maybe you were debating whether to fine-tune or retrain a model, or choosing between supervised and semi-supervised approaches. Set the scene the way you would internally – what you knew, what you didn’t, what was at stake.
Then, ask them to think out loud. You’re not necessarily looking for the “right” answer, you’re looking for how they approach decision-making.
Watch how they scope the problem. How they balance experimentation vs. engineering. Whether they consider edge cases. Whether they challenge assumptions you gave them and how they explain their reasoning.
The best candidates will naturally ask the kind of questions your team is already asking. That’s the point.
The “hot seat” scenario
In early-stage ML teams, no one hands you a perfect problem with perfectly labelled data and a clean objective function. You’re navigating trade-offs. You’re debugging in the dark. You’re working with product and trying to ship – not optimise for leaderboard accuracy.
That’s why dynamic, conversational interview formats are so valuable.
One approach we like is the “hot seat” – where you present the candidate with a vague, open-ended issue, e.g. model performance has suddenly dropped in production, and let them lead the investigation. As they talk, you introduce more variables. Maybe data drift, maybe edge cases, and maybe a new product requirement that changes everything.
This format reveals how they operate under uncertainty. Are they systematic or scattered? Do they default to intuition or ask for data? Can they explain what matters and why – even when everything is a bit murky?
It also shows how comfortable they are collaborating in real time, which is critical if you’re building in-person or hybrid.
Rapid take-homes with immediate feedback
We get it – take-homes have a bad reputation. Candidates hate them when they’re poorly scoped, and founders often stop using them because the return on effort feels low.
But the problem isn’t the take-home. It’s the design.
A short, focused task that mirrors real work and includes proper instructions is still one of the best ways to evaluate how someone thinks when they’re not on the spot.
Want to see how they handle noisy data? Give them a real messy CSV and a tight deadline. Want to assess trade-off thinking? Ask them to make modelling decisions with imperfect metrics and write up their assumptions.
Here’s the key though: follow up fast. A take-home without feedback feels transactional. A take-home with feedback becomes part of the process. It also gives the candidate a stronger sense of your team’s culture – how you review work, how you communicate, and how you treat people’s time.
Balanced interview framework for Machine Learning roles
If you’re hiring multiple ML roles or trying to standardise your process across a team you need something consistent that still gives you room to flex.
Here’s a structure that scales well and still feels personalized:
-
Kick-off with a narrative: Set the tone with a short walkthrough of a real ML challenge you’ve tackled. Keep it informal but specific – this creates common ground.
-
Technical deep dive: Ask the candidate to explain how they’d approach a similar challenge. Let them talk through trade-offs, modelling choices, and validation steps.
-
Peer pairing session: Introduce them to someone from your team for a 30-minute working session. Could be data cleaning, exploratory analysis, or just walking through an architecture diagram. It’s not a test, it’s a vibe check.
-
Close with clarity: Let the candidate ask questions. Talk through next steps. Give space for concerns or curiosity. End on a collaborative note, not a cliffhanger.
This framework works whether you’re hiring your first ML engineer or your fifth, and helps reduce bias by giving every candidate a consistent experience.
Why these frameworks work
Because they reflect the actual work. ML hiring isn’t about solving toy problems. It’s about testing someone’s judgment, curiosity, and ability to work in the kind of ambiguity you live in every day as a founder.
The best engineers won’t always “ace” a conventional interview. They might pause longer. Ask more questions. Think out loud before they commit. That’s not a weakness, that’s a signal.
Frameworks like these give those signals room to breathe.
They also create better candidate experiences. If your interview process feels sharp, respectful, and energising — you’ll close more offers, full stop.
And that’s where hiring velocity starts to snowball.
Don’t forget the questions
Frameworks are structure. But it’s the questions inside them that surface insight.
If you’re stuck on what to ask or how to go beyond “tell me about a time when…”, check out our guide. We break down real prompts that reveal how candidates reason, collaborate, and make decisions under pressure.
Need help building a hiring framework that fits your team?
Codexo partners with early-stage AI startups in San Francisco to help shape not just who they hire, but how they hire – from founder-led strategy to scalable process design.
If you’re ready to hire ML engineers but want to do it right from day one, let’s talk.