The Great Recalibration: America's Job Market After the Layoff Wave
The US job market in 2026 is living through what economists are calling the Great Recalibration. The tech layoff wave of 2023-2024 — which saw 400,000+ workers cut from Meta, Google, Amazon, Microsoft, and hundreds of startups — has given way to a more selective, AI-focused hiring landscape. On r/cscareerquestions and Blind, the mood has shifted from panic to cautious recalibration: the jobs are coming back, but they're different jobs demanding different skills.
The H-1B visa lottery remains the defining bottleneck for international tech talent. With 780,000 registrations competing for 85,000 annual slots, the system is essentially a lottery — a 10.9% chance, regardless of qualifications. Highly skilled engineers from India, China, and globally are increasingly choosing Canada, UK, or Germany over the US purely because the immigration pathway is more predictable.
At-will employment — the American system where either party can terminate the relationship without cause — creates a fundamentally different dynamic from European markets. Layoffs can happen with zero notice. But the flip side is equally powerful: Americans switch jobs more frequently, negotiate harder, and treat every role as a 2-3 year chapter rather than a career commitment. The average American changes jobs every 2.8 years.
For those with the right skills, America's job market remains the highest-paying in the world. A senior ML engineer at a FAANG company in San Francisco commands $300-500K total compensation (salary + RSUs + bonus). But the cost of entry — elite credentials, LeetCode grinding, 6-8 interview rounds — is staggering. The US rewards the obsessively prepared and punishes the casually competent.
United States
FAANG Is Hiring Again — But Only for AI
The 2023-2024 tech layoff wave was traumatic but temporary. By 2026, the major tech companies are hiring again — but with a sharp pivot toward AI-native roles. Meta is building out its AI research division aggressively. Google is restructuring around Gemini. Amazon's AWS is betting on ML infrastructure. The jobs are back, but they're not the same jobs. General software engineering roles are fewer; AI/ML specialisation is the new entry ticket.
On Blind and r/cscareerquestions, the post-layoff survivors describe a changed culture. Return-to-office mandates at Amazon, Google, and Meta have triggered a quiet migration to remote-friendly companies or relocation from SF/NYC to Austin, Miami, Raleigh, and Denver. The geographic decentralisation of tech is real and accelerating.
The tech layoffs didn't kill the market — they killed the idea that a CS degree alone guarantees a $200K job. Now you need AI skills too.
The H-1B Lottery: 780,000 Dreams, 85,000 Slots
The H-1B visa system is the single most frustrating aspect of the US job market for international talent. With 780,000 registrations in the latest lottery for 85,000 slots, the system is mathematically absurd — a 10.9% chance regardless of whether you're a PhD from MIT or a bootcamp graduate. Companies sponsor and pray; candidates wait and hope.
The practical impact: international students increasingly choose the O-1 (extraordinary ability) route, pursue EB-1/EB-2 green cards through employer sponsorship, or simply leave the US for countries with predictable immigration pathways. Canada's Express Entry, Germany's Fachkräfteeinwanderungsgesetz, and the UK's Global Talent Visa are all benefiting from H-1B frustration. America's loss is genuinely the world's gain.
| Hottest Sectors | AI · Healthcare · Cyber |
| Top AI Comp (FAANG) | $200-500K TC |
| H-1B Success Rate | ~11% |
| Healthcare Vacancies | 1M+ unfilled |
| Emerging Tech Hubs | Austin · Miami · Raleigh |
LeetCode, Networking, and the STAR Method: The American Playbook
The American job search has its own distinct rituals. For tech: LeetCode grinding (solving 200-400 algorithmic problems) remains the de facto entry exam for FAANG and tier-1 companies. System design interviews are the next gate for senior roles. The STAR method (Situation-Task-Action-Result) structures behavioural interviews across every sector.
Networking is more decisive in the US than any other English-speaking market. LinkedIn is used aggressively — not just for applying, but for direct outreach to recruiters, hiring managers, and team leads. Referral bonuses ($2-10K at most tech companies) mean employees are financially incentivised to refer candidates. A warm referral skips you past the resume screen entirely.
For non-tech sectors, geographic arbitrage is the winning strategy. A registered nurse in San Francisco earns $120K but spends $3K/month on a studio apartment. The same nurse in Dallas earns $85K but pays $1,200/month for a two-bedroom. Student loan repayment, housing costs, and state income tax (0% in Texas, Florida, Washington vs 13.3% in California) make location the most impactful financial decision in an American career.
✦ The SUAR Verdict — United States
The US offers the highest ceiling and the highest stakes in the global job market. SUAR's AI interview simulator is perfectly calibrated for the STAR method and competency-based interviews that American employers demand. The LeetCode-style technical preparation, combined with behavioural training, gives candidates the edge in the most competitive job market on Earth.