Startups have become the main route into Big Tech: how AI is reshaping IT hiring

Hiring at large technology companies has stayed tight for a second year in a row: Google, Meta, Microsoft and Amazon have cut thousands of positions, and the new ones they open come with a far higher bar. The main reason is the adoption of AI, which now handles tasks that used to occupy dozens of juniors and mid-level engineers.

Big Tech has closed its doors

The 2023–2025 wave of layoffs removed more than 300,000 jobs in the tech sector. AI tools automated a significant share of coding, testing and analytics work, and companies concluded they can do the same with fewer people.

• Google cut 12,000 employees in 2023 and ran a second round in 2024
• Meta cut more than 21,000 positions over two years
• Amazon eliminated over 27,000 roles in AWS and retail tech
• Microsoft laid off 10,000 people as it reorganized around Azure AI

New openings at the same companies require experience with LLMs, fine-tuning, RAG systems and evaluation frameworks, skills that only a handful of people worked with at scale 18 months ago.

Startups are short of talent

By contrast, AI and enterprise-software startups face an acute shortage of engineers. They need to build products fast, usually within 6–12 months before the next funding round. Hiring an experienced FAANG engineer is hard: those people value stability and large RSU grants. So startups look more widely, at candidates with the right mindset rather than only a specific stack.

According to LinkedIn Job Trends, in the first quarter of 2026 the number of postings tagged "generative AI" at companies with up to 200 employees grew 34% year over year. At large companies (more than 1,000 employees), the same figure fell 11%.

AI startups in enterprise automation, legal tech and healthcare AI are especially active. They need engineers who can deploy LLMs in regulated industries, and finding such people among recent graduates is not easy.

"I spent four months sending résumés to Google and OpenAI with no result.

Then in three weeks I got two offers from AI startups, with pay higher than I expected," reads a typical post on Reddit/cscareerquestions.

Why a startup is a strategically sound move

Working at a startup is not a plan B. For many engineers it is the best career route:

• Real AI experience from day one. At a startup, an engineer builds a RAG pipeline, picks embedding models and ships them to production in a single sprint. At a large company, that can take a year of approvals.
• A broad stack. One engineer often covers backend, ML inference and DevOps at once, and that experience is highly valued when moving to Big Tech.
• Equity. Startups before Series B often offer 0.1–0.5% of the company. With a successful exit, that can far exceed any FAANG RSU grant.
• Speed of growth. A senior position within 18 months is a real story. At Google, the median path to it takes 3.5–4 years.
• Visible results. A feature reaches users within a week, which matters for both a portfolio and professional motivation.

The main risk is instability: about 90% of startups do not survive five years. Choose ones with a working product, paying customers and a confirmed funding round within the past 12 months.

What this means

The IT hiring market has reshaped itself: Big Tech has become an elite club with strict filters, while startups have become a live market that needs people right now. For engineers, this means the path to Google or Microsoft now often runs through 2–3 years of real AI experience at a startup, rather than through an entry-level position directly.

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