Hiring a Machine Learning Engineer is one of the most high-leverage decisions a founder can make, especially in the early days of building a company. The right person will not only ship models that matter, but shape your technical culture, move with urgency, and deliver value to end users fast.
At Codexo, we partner with startups and scaleups across San Francisco to hire exceptional ML talent. And from our experience, the most effective founders aren’t just technical – they ask sharp, calibrated questions that uncover how candidates think, what drives them, and whether they can thrive in a fast-moving startup environment.
Here are five questions we recommend every founder ask ML candidates in 2025.
1. Can you walk me through a machine learning project you owned end-to-end?
At early-stage startups, you’re not hiring someone to sit in a silo. You need someone who can move across the full ML lifecycle – from defining the problem and exploring the data, to modeling, deployment, and iteration in production.
A strong answer here will reveal how a candidate thinks about impact, how they navigate ambiguity, and how comfortable they are with ownership beyond just the model.
This question separates candidates who’ve only worked on narrow slices of a project from those who can operate across the stack and take real-world systems live.
2. How do you keep up with developments in the machine learning space?
Machine learning evolves fast and the best engineers make it a habit to stay on top of new research, tools, and practices.
Whether through reading arXiv papers, contributing to open-source libraries, running side projects, or experimenting with new architectures, keeping up is essential. According to recent skill-shift research, roughly 75% of key job skills change over a three-year period, underscoring how vital continuous learning is, especially in fields like ML.
This question indicates how well someone will adapt as the field shifts.
3. Tell me about a tradeoff you had to make in a recent ML system. What factors drove your decision?
Every production system comes with constraints – compute limits, latency requirements, noisy data, tight deadlines.
This question surfaces whether a candidate can think beyond the academic or theoretical, and make decisions rooted in the needs of the product or the user.
Look for thoughtful, nuanced answers. You want to hear how they balanced competing priorities – accuracy vs. speed, interpretability vs. complexity, innovation vs. stability – and why they chose the path they did.
This is the kind of thinking that sets apart strong ML builders from those who’ve only worked in research settings.
4. What kind of team environment helps you do your best work?
You’re not just hiring for skills, you’re hiring for how someone will show up inside a small, fast-paced team.
Top-tier ML Engineers are often drawn to three things: working with smart people they can learn from, solving meaningful problems, and being close to the decision-makers, especially technical founders with strong track records.
This question gives you early signals on what motivates them, how they collaborate, and whether they’ll thrive in your environment.
If your team is highly collaborative and you need someone who communicates well across engineering, product, and research — now’s the time to check for that alignment.
5. What would make this role a clear yes for you?
It’s a direct question, but it often leads to the most honest and useful answers in the process.
Candidates who are clear on what they want – whether that’s ownership, technical challenge, mentorship, or the opportunity to build alongside a founder they respect – tend to perform better and ramp faster.
It also helps you de-risk surprises later. If someone is heavily motivated by mentorship but your team is currently two engineers, you want to surface that now, not post-offer.
More than anything, this question opens up a transparent conversation about fit on both sides.
Ready to hire exceptional ML talent?
Startups don’t have time for guesswork when it comes to hiring, and in San Francisco’s highly competitive ML market, strong candidates are rarely on the market for long.
Asking the right questions in early interviews gives you a clearer picture of who a candidate is, how they think, and what they value. It also shows them that you’ve done this before – that you’re thoughtful, intentional, and building something worth joining.
At Codexo, we help founders cut through the noise and connect with ML Engineers who thrive in high-ownership, high-impact environments. Whether you’re pre-seed or post-Series A, we’re here to make hiring a strength, not a bottleneck.
We partner with startups and scaleups across San Francisco to find, attract, and close world-class Machine Learning Engineers, Research Engineers, and Applied Researchers. Let’s talk – contact us to get started.