The US AI talent market in 2025 is simultaneously tight and misread. Tight because genuinely capable AI engineers are in high demand. Misread because most hiring managers are screening for credentials and titles rather than capabilities.
We've placed over 120 AI engineers across enterprise clients in the past 18 months. The hiring processes that work look nothing like traditional software engineering hiring. Here's what does and doesn't work.
What Doesn't Work
LeetCode-style algorithmic interviews — The skills required to ship AI systems in production are not the skills tested by graph traversal problems. Companies that rely heavily on whiteboard algorithm interviews are filtering out strong applied AI engineers who haven't practiced competitive programming.
Over-indexing on ML theory — There's a meaningful difference between an engineer who can implement backpropagation from scratch and an engineer who can deploy and iterate a fine-tuned LLM in a production environment. Most enterprise AI problems need the latter.
Resume keyword matching — 'Experience with LLMs' on a resume means everything from building GPT-4 wrappers to fine-tuning domain-specific models on proprietary datasets. Filtering by keywords without behavioral depth interviews misses the best candidates.
What Works
Capability-based screening — Before a phone screen, send a short (30-minute) practical assessment: give them a real API, a real task, and see what they build. The quality of what they ship in 30 minutes tells you more than 3 rounds of interviews.
Production experience questions — 'Tell me about the last AI system you shipped that broke in production. What happened, and what did you do?' Engineers who've shipped real systems have real answers. Engineers who haven't give theoretical answers.
Architecture discussions with real constraints — 'We have 50,000 customer support tickets per month, a $15K/month AI infrastructure budget, and a 3-month timeline. Walk me through how you'd build an automated triage system.' Listen for tradeoffs, not textbook answers.
Compensation Reality in 2025
Strong applied AI engineers at the senior level command $180-240K base in major US markets, with total compensation $250-350K at well-funded companies. If your compensation band tops out at $150K for an AI engineering role, you're fishing in a smaller pond than you think.
The good news: mid-level applied AI engineers with 2-3 years of experience are more available than senior engineers, and with the right onboarding and mentorship, they ramp faster than the market gives them credit for. Building for depth at the senior level and breadth at the mid level is the team structure that scales.