How It Works

All salary figures come from 261,000+ H-1B Labor Condition Application (LCA) filings, covering fiscal years 2023–2026. These are public records submitted by employers to the U.S. Department of Labor when sponsoring foreign workers. Each record includes the job title, employer, worksite state, prevailing wage level (I–IV), and the actual base salary offered.

Important: These are base salaries only — they do not include stock, bonuses, or benefits. At major tech companies, total compensation is typically 50–200% higher than base salary. All ranges shown on the calculator reflect base salary only.

Bias note: H-1B data skews toward large tech employers and specific roles that sponsor visas. It may underrepresent early-stage startups, non-STEM roles, and companies that don't sponsor H-1Bs. Use these numbers as directional guidance, not definitive offers.

Median, Not Average

We use the median (50th percentile), not the mean (average). The mean is sensitive to outliers — a few executive salaries can inflate it significantly. The median represents what a typical person in that role actually earns.

Linear Interpolation

We fetch all matching salary records, sort them, and compute percentiles using linear interpolation between adjacent ranks — the same method used by the U.S. Census Bureau and BLS. This produces accurate p25, p50 (median), p75, and p90 values that are robust even with unevenly distributed data.

Experience Level Mapping

Your years of experience map to a Prevailing Wage (PW) Level (I–IV), matching the government's own classification system:

We query salaries for your specific PW level. If fewer than 100 records exist at that level, we fall back to the overall market median for your role — which is more stable with small samples.

📍 State / Location

Source: Calibrated from actual per-state median salaries in our H-1B database (June 2026). Each state's multiplier = state_median ÷ national_median.

Example: CA's median is $181K vs a national median of $163K → 1.11× multiplier (not 1.18× as sometimes assumed). Washington tracks exactly at the national median (1.00×) despite Seattle's tech scene.

🎓 Education

Source: Estimated from general labor market patterns. H-1B data does not include education level per individual, so these premiums cannot be derived from our database.

🏢 Company Type

Source: Estimated. These reflect the observation that FAANG companies pay above-market base salaries while startups compensate more heavily in equity.

🛠️ Skills

Source: Estimated demand multipliers based on general market patterns. Each skill has a premium reflecting how much employers typically pay for that expertise relative to the base role.

Diminishing Returns

Employers hire for a role, not a checklist. Each additional premium skill adds less value. We apply a decay curve:

Total skill bonus is capped at 25% — the best skill profile in the world doesn't earn more than a 25% premium for the same role and experience level.

Full Skill Premium List

The MVS is a composite score from 0–100 reflecting your profile's competitiveness — it is not a dollar amount. It combines five sub-scores:

• Base salary only. Total compensation at major tech companies (including RSUs, bonuses, and benefits) is typically 50–200% higher. All figures shown are base salary.
• H-1B visa bias. The data represents visa-sponsored workers, not the general workforce. It skews toward large employers, specific roles, and may underrepresent domestic-only candidates.
• Geographic concentration. California alone represents 37% of all records. State-level medians for smaller states may be based on fewer data points.
• No individual-level detail. H-1B data doesn't include education level, specific skills, or years of experience per individual — only the job title and prevailing wage level. We infer experience from the PW level and estimate education and skill premiums separately.
• Education, skills, and company tier premiums are estimates. Unlike salaries and state multipliers (which come directly from our database), these adjustments are based on general market observation and should be interpreted as directional, not precise.
• Not a job offer predictor. This tool provides market context, not a guarantee of what any specific employer will pay. Actual offers depend on negotiation, interview performance, competing offers, and company-specific compensation bands.