AI infrastructure hiring is the strongest segment in US tech

US technology hiring has split sharply. Infrastructure, platform and ML operations teams are expanding, while product engineering headcount in large employers remains broadly flat year on year.

Candidates increasingly ask about compute budgets and model access during interviews, treating them as career-development factors alongside compensation.

Employers outside the major hubs are winning candidates by offering ownership of whole systems rather than narrow slices of a platform.

In technology, the practical consequence is that briefs written a year ago no longer match the market. Roles defined as broad, generalist engineering attract volume without depth, while narrowly specified senior roles attract almost nobody through advertising alone. We usually recommend splitting the requirement: define the two or three capabilities that genuinely cannot be trained, then treat everything else as developable.

Across the US, the pattern is regional rather than national. Aggregate figures hide markets that are still extremely tight, so we benchmark by metro and sector rather than by country-level averages.

If you are hiring in technology right now, the practical takeaway is to shorten your process and be explicit about what is genuinely non-negotiable. Fewer, better matched candidates almost always beat a longer list — and in a market moving this quickly, speed of decision is itself a competitive advantage.