How we build our salary data.

For each of our 3,445 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.

Every role in the Orbyt catalog is mapped to a BLS Standard Occupational Classification (SOC) code, which provides the national baseline salary. From there, three multipliers adjust the figure:

Level multipliers

Industry multipliers (sample)

These multipliers are calibrated against H-1B LCA wage data and validated annual. The level multipliers reflect median salary ratios observed between seniority levels in the OES dataset. Industry multipliers reflect the premium or discount that specific sectors pay relative to the cross-industry median for the same role.

Our catalog includes two tiers of roles. Curated roles have hand-written salary drivers, total compensation notes, career ladder narratives, and custom FAQ pairs reviewed by our editorial team. Derived roles use the same BLS baseline methodology and produce the same salary accuracy, but do not yet include editorial content.

Both tiers receive identical treatment for city-level cost-of-living adjustments, experience band calculations, and annual data updates.

Every role in the Orbyt catalog is mapped to a Standard Occupational Classification (SOC) code. This mapping serves two purposes: it provides the salary baseline for derived roles, and it enables citation traceability from any Orbyt salary figure back to the underlying government data source.

The SOC code for each role is displayed on its detail page and included in the JSON-LD structured data. API responses from the Intelligence API also include the SOC code when available.

Emerging role mapping: The BLS SOC system covers approximately 800 occupation categories. Many modern tech roles, especially in AI, blockchain, and spatial computing, do not have dedicated SOC codes. These roles are mapped to the closest general category (e.g., AI Agent Engineer maps to SOC 15-2051, Data Scientists and Mathematical Science Occupations) with the industry and level multipliers accounting for the salary premium.

Every role page now shows a structured total compensation breakdown with five components:

• Base salary (25th, 50th, 75th percentiles)
• Equity / year — median annual RSU or option vesting value, calibrated by role seniority and category
• Annual bonus — as a percentage of base, reflecting role-specific compensation norms
• Signing bonus — typical one-time new-hire bonus for the role
• Total compensation — base + equity + bonus combined

The ratios are category-specific: AI/ML roles carry 25-35% equity weight, executive roles 30-45%, general engineering 10-20%, and non-technical roles 5-10%. These ratios are derived from H-1B LCA filings, SEC proxy statements, and aggregated self-reported data.

Every role page shows estimated base salary by company size. The same role at a startup versus a public company can differ by 20-40% in base salary (offset by equity composition). Our company size multipliers are derived from H-1B LCA filings segmented by employer headcount and correlated with SEC-reported median employee compensation.

Each role carries a remote salary multiplier reflecting the typical pay differential between on-site and fully remote positions. These multipliers range from 0.80 (20% pay reduction) for roles where remote work is less common, to 0.95 (5% reduction) for executive and high-demand AI roles where talent scarcity limits employers' ability to discount.

Remote multipliers are calibrated from job posting data comparing salary ranges on listings tagged “remote” versus “on-site” for the same role title. Some companies offer flat national rates regardless of location, which is not captured in the multiplier.

Salary data is reviewed and updated annually. 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 annual. Job posting data is refreshed continuously. Self-reported data from Orbyt users is incorporated on a rolling basis.

Every salary figure on Orbyt is an estimate built from public sources, not a measurement of every job in the U.S. Here is what sits behind a typical role and city cell.

We are deliberate about confidence signals. An honest number under 500 beats a fabricated number over a million. When coverage is thin, the page says so. No blended "millions of data points" marketing claim will appear here, ever.

Recency per cell: BLS OES updates annually (May release, with a 12 to 18 month lag between data collection and publication). H-1B LCA data updates quarterly. Job posting scrapes refresh continuously. Any role and city cell blends these three sources, weighted by confidence. The footer of every salary page shows the latest data refresh date.

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).
• BLS SOC codes cover approximately 800 occupation categories. Specialized tech roles (AI Agent Engineer, Zero Knowledge Proof Engineer) are mapped to the closest general SOC code. The multiplier methodology compensates for this gap, but emerging roles inherently have less historical data.

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.