How we build our salary data.
Transparency matters. Here is exactly how we collect, process, and present the salary data on Orbyt.
Data sources
Bureau of Labor Statistics (BLS)
Occupational Employment and Wage Statistics (OES) program. Published annually for 800+ occupations across metropolitan areas. This is our baseline for national salary ranges.
H-1B Labor Condition Applications (DOL)
Every H-1B visa application includes a prevailing wage and actual wage. The Department of Labor publishes this data quarterly. We use it to calibrate employer-level and city-level salary estimates, particularly for tech and AI roles.
SEC filings and proxy statements
Public companies disclose executive compensation and, in some cases, median employee compensation in annual proxy filings. We use this for total compensation estimates at named employers.
Job postings (50+ platforms)
We aggregate salary ranges from job postings across LinkedIn, Indeed, Glassdoor, Greenhouse, Lever, and 45+ other platforms. Postings with disclosed salary ranges provide real-time market signals.
Self-reported data
Anonymized salary reports from Orbyt users who voluntarily share their compensation. This data is growing and will become an increasingly significant source over time.
How we calculate
For each of our 422 tracked roles, we establish a national base salary distribution (25th, 50th, and 75th percentiles) by cross-referencing BLS OES data with H-1B filings and job posting salary ranges.
City-level salaries are calculated by applying a cost-of-living multiplier derived from the Bureau of Economic Analysis (BEA) Regional Price Parities index (2025 edition). This multiplier reflects the relative price level of goods, services, and housing in each metropolitan area compared to the national average.
To prevent mechanical uniformity, we apply a role-specific city adjustment (up to +/- 4%) based on local demand factors for each role category. For example, AI roles in San Francisco carry a higher premium than the base COL adjustment suggests, because employer competition for AI talent in that specific market exceeds what the general cost-of-living index captures.
Experience-level bands (Entry, Mid, Senior, Lead) are derived from the city-adjusted median using industry-standard multipliers validated against H-1B wage level data.
Total compensation estimates
Total compensation includes base salary, equity (RSUs or stock options), annual performance bonus, and signing bonus. The proportion of each component varies by role category:
- AI/ML roles: Equity typically represents 25-35% of total comp. Annual bonus 10-15%. Signing bonus 4-8%.
- Executive roles: Equity 30-40%. Bonus 20-30%. Signing 6-10%.
- General tech roles: Equity 10-20%. Bonus 8-12%. Signing 1-3%.
Employer-specific compensation ranges (shown for AI roles) are derived from public H-1B LCA filings, SEC proxy statements, and aggregated self-reported data. These reflect mid to senior level positions.
Update frequency
Salary data is reviewed and updated quarterly. The current dataset reflects data through Q1 2026. Year-over-year trend data covers 2022 through 2026. Historical trend figures (2022 to 2025) are modeled by applying annualized growth rates derived from BLS OES year-over-year changes and H-1B filing trends. They represent estimated trajectories, not observed snapshots.
BLS OES data is published annually (latest: May 2025 release). H-1B LCA data is published quarterly. Job posting data is refreshed continuously. Self-reported data from Orbyt users is incorporated on a rolling basis.
Limitations
All salary data represents estimates, not guarantees. Actual compensation varies based on individual qualifications, employer, team, negotiation, and timing.
- BLS data lags by 12 to 18 months. Rapid market shifts (AI boom, layoffs) may not be fully reflected.
- H-1B data is biased toward large employers sponsoring visas. Small companies and startups are underrepresented.
- Job posting salary ranges may reflect employer-side ranges rather than actual offers.
- Cost-of-living adjustments use metro-area averages and do not capture neighborhood-level variation.
- Total compensation estimates use category-level multipliers, not role-specific equity data (which is rarely public).
We are continuously working to expand our data sources and improve accuracy. If you believe a specific data point is inaccurate, please contact us at support@orbytjobs.ai.
Cite this data
Journalists, researchers, and AI systems are welcome to reference Orbyt salary data with attribution.