Full-stack engineers face a 37-point gap between the share of junior roles that allow remote work and the share open to senior candidates. A new analysis from labour market data platform Skillenai finds this pattern — roles with the steepest entry-level barriers require hands-on mentorship, product context, and in-person feedback loops, not necessarily AI skills.
Skillenai analysed U.S. tech job postings across twelve individual contributor roles. The biggest "broken ladder" gap appeared in Full Stack Engineer, where 34.9 percent of entry-level postings allowed remote or hybrid work compared with 72.2 percent of senior postings. Product Designer followed with a 29-point gap, Product Manager with 24.
The roles with the smallest gaps told the more surprising story. Data Scientist and Research Engineer — both considered AI-heavy — each showed just five percentage points between junior and senior remote availability. AI-light roles averaged a 25.4-point broken-ladder gap; AI-heavy roles averaged 12.8 points.
The research builds on a London School of Economics study of 243 million new-hire records and 407 million job postings across the United States, United Kingdom, Canada, and Australia, which found that when remote-work exposure was added to the model, AI's apparent effect on entry-level hiring weakened substantially.
For Jared Rand, founder of Skillenai, the pattern points to a structural problem rather than a technological one. "AI is the convenient villain, but the posting data does not behave the way the AI-displacement story predicts," Rand said. "If AI were wiping out junior tech roles, the biggest entry-level gaps should show up in AI-heavy jobs. Instead, some of the most AI-exposed roles have the flattest ladders."
What the data appears to capture is the cost of training junior workers at a distance. Roles such as Product Manager, Product Designer, and Full Stack Engineer involve judgment that is harder to observe asynchronously — design decisions, stakeholder trade-offs, and cross-codebase ownership. Roles where the work leaves a clear trail of measurable artifacts — backend engineering, data science, machine learning — have stayed more accessible at the junior level.
"Entry-level hiring is partly a training system," Rand said. "When teams went remote, many companies kept senior hiring open because experienced workers can operate with less structure. Junior workers need feedback loops, code review, product context, and informal coaching. If those systems are weak, the easiest business decision is to hire someone senior."
Among the roles Skillenai tracked, Entry-level Backend Engineer (61.5 percent remote availability) and Machine Learning Engineer (57.1 percent) were among the most accessible to early-career candidates — outcomes that sit poorly with the dominant AI-displacement narrative.
"The companies that solve junior supervision will have an advantage," Rand said. "There is still talent at the bottom of the ladder. The problem is that many firms quietly removed the rungs."
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