USA leads with $132,995/yr avg, followed by Singapore ($116,955) and Australia ($110,550). India ($43,106) and Brazil ($53,705) are 3–4× lower — reflecting cost-of-living adjusted markets. Bonus structures mirror salary: USA averages $17,939 in annual bonus.
Bonus as % of salary is ~13.5% consistently across all countries. Total compensation gap between USA and India exceeds $100K/yr.
USA total comp (salary+bonus) = $150,934 vs India = $48,973. A 3.08× gap.
Generative AI ($97,097) and LLM ($97,077) command the highest average salaries — reflecting current market demand. Reinforcement Learning ($95,424) sits lowest. The spread across specializations is surprisingly narrow ($1,673), suggesting geography > specialization as salary driver.
Research Scientists earn the most ($109,798), followed by ML Engineers ($102,122). Data Analysts earn $69,429 — a $40K gap vs Research Scientists. Role choice is a stronger salary predictor than specialization.
Career progression shows a near-linear salary gain of ~$5,200/year. Entry (0 yrs): $58,837 → Senior Lead (19 yrs): $160,823. The steepest jump occurs in the 0–5 year window (+$26K, ~44% gain), flattening somewhat at senior levels.
The Entry → Mid jump is +$16,084 (+26%). Mid → Senior: +$27,196 (+35%). Senior → Lead: +$37,711 (+36%). Lead roles at $142,319 are the peak band.
Average +$5,200 per additional year of experience across the full 0–19 year range.
Counterintuitively, education level shows minimal salary differentiation in this dataset. Bootcamp grads ($97,206) slightly outperform PhD holders ($96,131). Salary percentiles are nearly uniform (~50.4–50.7 across all education levels). This suggests skills and experience outweigh credentials in the AI job market.
Consulting (51.0) and Energy (50.9) lead skill demand scores. All industries cluster tightly in the 50.1–51.0 range, indicating universally high AI demand across all sectors — no industry is being "left behind."
Healthcare, Telecom, and Energy each average 17.6 openings per listing — the highest. Finance and Tech follow closely. Education has the fewest at 17.4. The tight range reflects democratized AI adoption across all sectors.
All countries average ~4.5 interview rounds with difficulty scores clustering at 54.8–55.3. Netherlands and Brazil show the highest difficulty (55.2–55.3) despite similar round counts. UK has the lowest difficulty (54.78) with the fewest rounds (4.46). The weak country-level correlation suggests difficulty is driven by role complexity more than geography.
All 12 countries have nearly identical hiring processes — reflecting a globally standardized AI interview culture.
Higher-funded companies offer marginally better job security: Low-funded firms avg 71.4 vs High-funded 76.4 (+7%). Layoff risk is uniformly ~18% across all funding tiers, suggesting funding level does not protect against market-wide layoffs in AI.
Layoff risk is 18% across all funding tiers. Funding size does not reduce layoff exposure — suggesting AI layoffs are driven by macro trends, not company cash.
Economic indices are remarkably uniform (72.3–72.7) across all 12 countries — this dataset normalizes for economic development. AI maturity years range 8.4–8.6 years, with Germany, Australia, France and Canada leading. Adoption scores: UAE leads at 71.7, USA trails at 71.1 — a counterintuitive finding suggesting emerging markets are adopting AI aggressively to catch up.
All three work modes produce nearly identical outcomes: satisfaction ~72.7, WLB ~69.1–69.2, salary ~$96.4–96.7K. Hybrid marginally leads in salary ($96,748). This data suggests work mode is not a significant quality-of-life differentiator in AI roles — job quality matters more than location.
Work mode choice has negligible impact on satisfaction or WLB in AI roles. The work itself — not location — drives experience.
A strong negative correlation exists between weekly hours and WLB. Workers at 35 hrs/week average a WLB of 84.2. At 40 hrs: 75.2 (-11%). At 50 hrs: 55.2 (-34%). At 55+ hrs: 50.2 — a dramatic collapse. Every additional 5 hours per week costs ~10 WLB points.
Every 5 extra hours/week = −10 WLB points. The 35→55 hr jump costs 34 WLB points — a 40% decline.
Startups have the fastest promotion speed (54.5) — nearly 2× faster than Enterprise (27.7). However, Enterprises lead in employee satisfaction (74.6). Career growth scores are relatively flat across sizes (57.1–57.4), with Startups edging ahead (57.4). Startups = fast promotion. Enterprise = stability + satisfaction.
| Company Size | Promotion Speed | Career Growth | Satisfaction |
|---|---|---|---|
| Startup | 54.5 ⚡ | 57.4 | 69.6 |
| Small | 36.6 | 57.1 | 73.1 |
| Medium | 36.6 | 57.2 | 73.2 |
| Large | 36.7 | 57.2 | 73.1 |
| Enterprise | 27.7 | 57.1 | 74.6 ⭐ |
Startup promotion speed is 97% faster than Enterprise, but Enterprise offers 7.2% higher satisfaction. Choose your priority.
Vacation days are nearly uniform across all countries at ~20 days/year. Brazil and UK lead with 20.1 days. France has the highest tax rate (27.3%) alongside the most competitive social benefits. USA has the lowest tax rate (26.8%) but equal vacation entitlements. The tax-vacation correlation is weak in this dataset — both high and low-tax countries offer ~20 vacation days.
Research Scientists and Software AI Engineers face the highest automation risk (50.6 each). Machine Learning Engineers and AI Engineers have the lowest risk (50.1 each) — perhaps because their work defines and directs automation rather than performing it. Scores cluster tightly in 50.1–50.6, suggesting all AI roles have broadly similar automation exposure.
NLP (50.9) is the highest-risk specialization — as NLP tasks are becoming increasingly automated by LLMs. Generative AI (50.0) and MLOps (50.2) show the lowest automation risk. The irony: GenAI practitioners are safest from the very technology they build.
NLP engineers (building language models) face the highest automation risk from language models. GenAI builders face the lowest.
Company rating is not a meaningful predictor of layoff risk — all rating bands from 3.0 to 5.0 show identical 18% layoff risk. Similarly, economic index and funding level produce negligible variation. This suggests AI layoff risk is primarily driven by macroeconomic factors and industry cycles — not company-specific attributes. No single measured variable strongly predicts layoff risk in this dataset.
All measured variables — rating, funding, size, country — show uniform 18% layoff risk. This is a systemic AI market condition, not a company-specific risk.
Global average offer acceptance rate is 75.0% — meaning 1 in 4 offers is declined. Work mode shows minimal variation: Remote leads at 75.1%, Onsite and Hybrid both at 75.0%. This flat distribution suggests work mode is not a primary factor in offer acceptance decisions for AI professionals — salary and role fit likely matter more.
A 25% decline rate is universal. Offering Remote vs Onsite won't change acceptance odds. Focus on compensation and role clarity to improve acceptance.
Average AI salaries have remained remarkably stable at ~$96,200–$96,890 from 2020–2026. 2020 peaked at $96,890, with slight compression in 2022 ($96,227) during the tech downturn. 2026 shows recovery to $96,659. The market suggests mature, stable compensation rather than exponential growth.
Based on this analysis, a salary prediction model should prioritize these features by importance:
A gradient boosted regression using country, experience_years, experience_level, and job_role should achieve strong predictive power (est. R² > 0.85).
The dataset is evenly distributed across work modes, representing a truly global hybrid workforce. All three modes offer equivalent outcomes in salary, satisfaction, and WLB for AI professionals. The remote work premium is negligible in AI — unlike other tech sectors where remote historically paid less.
For maximum AI career ROI: Move to the USA or Singapore, target Research Scientist or ML Engineer roles, accumulate 10+ years experience, choose a Startup for speed or Enterprise for stability, and keep weekly hours under 45.