We analyzed 2.3 million job postings from the past 12 months across LinkedIn, Indeed, Greenhouse, and Lever to map the shifting landscape of AI-adjacent tech roles. The results challenge several popular narratives.
Growing Fast: AI Infrastructure Engineers
The hottest role isn't 'AI Engineer' — it's the person who makes AI systems run reliably in production. Postings for MLOps, AI Infrastructure, and ML Platform roles grew 67% year-over-year. These roles command $180-240K total compensation at mid-level, reflecting the acute shortage of engineers who understand both ML systems and traditional infrastructure.
Transforming: Data Scientists
The traditional data scientist role — someone who builds models in notebooks and hands them to engineering — is rapidly disappearing. In its place, we see two divergent paths: ML Engineers who own the full model lifecycle, and Analytics Engineers who focus on data modeling and business intelligence. Pure 'data science' postings dropped 23%, while these successor roles grew 34% and 41% respectively.
Surprising Resilience: Frontend Engineers
Despite AI coding assistants improving rapidly, frontend engineering postings held steady (+2% YoY). The reason? AI-powered products need sophisticated user interfaces, and the complexity of modern frontend development — accessibility, performance, state management — remains difficult to fully automate.
Salary Trends
Median total compensation for senior AI/ML roles crossed $250K nationally for the first time, with Bay Area and NYC commanding 25-40% premiums. Remote-first companies are narrowing this gap, offering 85-95% of hub salaries regardless of location.
