Methodology
How OfferUnlock estimates the market value of an offer — and the limits we own up to. This page is deliberately transparent: if a number looks off to you, we want to know.
In one sentence
OfferUnlock computes a market estimate from your base salary, role, location, and experience, then places your offer in a range around that estimate. The calculation runs in your browser — your numbers are not sent to anyone.
Upfront honesty
Our estimates are calibrated from public sources — they are not proprietary primary data.
We don't have access to the internal datasets of Levels.fyi, Glassdoor, or Payscale. What you get is based on:
- a reference base salary anchored on the median of a mid-level tech profile (3 to 5 years of experience) in the United States, across all industries;
- adjustments for location, role, and experience, calibrated by cross-referencing public sources (BLS, Statistics Canada) and market observations (Levels.fyi, Glassdoor).
What we don't claim to do:
- Give a value accurate to within 5%. Our ±12% range around the market deliberately reflects the noise in public sources.
- Cover every case. The product targets salaried tech offers in North America. European and Asian markets, highly specialized roles, and exotic packages (complex pre-IPO stock options) are outside our current scope.
- Provide week-to-week up-to-date figures. The public benchmarks themselves aren't.
If a number seems off from your experience, write to privacy@offerunlock.app. Documented counter-examples help us improve the calibration.
Sources consulted
Official sources
| Source | What it provides |
|---|---|
| U.S. Bureau of Labor Statistics — Occupational Employment & Wage Statistics | Median salaries by occupation, updated annually. This is the anchor of our reference base. |
| Statistics Canada — Labour Force Survey | Canadian equivalent for Canada estimates. |
| Levels.fyi (public views) | Medians by level and by company in tech. Read directly from public pages. |
| Glassdoor (public views) | Medians by title and by city. Documented upward bias (contributors are over-represented at large companies). |
Cross-check sources
We cross-reference this data with annual sector reports (Hired State of Salaries, Stack Overflow Developer Survey, Robert Half / Hays Tech barometers) to verify the consistency of gaps by function and by experience.
Limits we own up to
- Selection bias. Levels.fyi and Glassdoor are fed by people who choose to share — often the best paid. The bottom of the range is under-represented there. Our reference base is therefore deliberately lower than their displayed median, to compensate.
- Time lag. The tech market went through severe compressions over 2024–2026. Medians published in 2024 on 2022–2023 data are already partly outdated. We recalibrate at least once a year.
- Geographic granularity. We group certain cities together (San Francisco and New York in the same tier, for example), even though a real gap exists between them. A trade-off we accept at this stage.
How the estimate is built
The estimate starts from a reference base of about USD 85,000 — the base salary of a generalist mid-level tech profile in the United States. This base is then adjusted by three factors.
The geographic adjustment
| Location | Effect on the estimate | Why |
|---|---|---|
| Very high cost area (SF, NYC) | +30% | Documented gap of 25 to 35% vs the continental median. |
| US remote role | +25% | These roles target candidates whose salary expectations are anchored on major metros, not on a national average. |
| High cost area (Austin, Seattle) | +10% | 10 to 15% above the US median depending on the city. |
| Canada, Europe, other | reference | National reference baseline. |
One important limit: the estimate is expressed in USD, including for Canadian or European cases. If you enter a salary in another currency, the result will be skewed. We plan to handle currency by region in a future version.
The role adjustment
Software engineering and product management get an upward adjustment (+15% and +10% respectively), consistent with public medians. Other roles use the reference base without a specific adjustment for now — finer per-role calibration is part of our planned improvements.
The experience adjustment
Experience increases the estimate non-linearly: about +5% per year over the first 15 years, then the effect drops sharply beyond that.
| Experience | Cumulative effect |
|---|---|
| Entry level | reference |
| 5 years | +25% |
| 10 years | +50% |
| 15 years | +75% |
| 20 years | +82% |
| 30 years | +98% |
Why this saturation? Because the pay of very senior profiles (Staff, Principal, Distinguished) rarely comes from a mechanical seniority effect. It depends on individual levers — equity, retention, key roles — that our general estimate doesn't try to model. A linear progression would value a 30-year profile at nearly 3x an entry-level one, which isn't borne out in the market.
The ±12% range
Once the estimate is computed, we place it within a ±12% range. Below it, your offer is considered below market; above it, above market; in between, within range.
This width absorbs the normal noise between public sources (the median gap between Levels.fyi and Glassdoor for the same role and city is 5 to 10%). A narrower range would produce false "below market" signals for candidates who are actually well positioned.
When we recalibrate
| Trigger | Action |
|---|---|
| Every year (May/June) | Review of the reference base and geographic adjustments. The tech market moves significantly from one year to the next. |
| Recurring user signal | If several people challenge the same case, it's a sign our calibration has drifted on that segment. |
| Before a high-traffic period | Consistency check against several public benchmarks. |
Contact
To challenge a number or help us refine the calibration, write to privacy@offerunlock.app. Any documented and sourced correction will be credited if published.