AI and Data
Hiring your first ML engineer
By Humand Talent Team · 20 June 2026
Your first ML engineer sets the technical direction for everything that follows. Hire the wrong shape of person and you'll spend the next 18 months untangling a research prototype that never made it to production — or a solid engineer who can't move the model forward.
Decide the shape first
Are you shipping models into a product, or discovering what the model should even be? A research-leaning first hire will publish, prototype and iterate on architectures. A platform-leaning first hire will get inference into production, instrument it, and make the second hire's life easier. Both are valid — pick deliberately.
What to test for
- Reasoning about data quality, not just model choice
- Comfort with the boring parts: evaluation, monitoring, cost per inference
- Judgement about when NOT to use ML
Common mistakes
Hiring for a paper record when you need someone to ship. Hiring an MLOps generalist when you actually need a research engineer. Paying senior FAANG rates for someone who's never owned a production system.