Remote Worker Salary: Geographic Pay Differences

The median software developer in San Jose earns $180,320 annually. In Austin, that same role pays $128,750. That $51,570 gap is not explained by cost of labor alone — and for remote workers earning $150k+, understanding which number your employer actually uses to set your base salary is worth real money.

Key Figures at a Glance

Sources: BLS Occupational Employment and Wage Statistics, May 2024; NBER Working Paper No. 33383, January 2025.

The Geographic Pay Gap Is Real — and Larger Than Most Employers Actually Apply

BLS OEWS data for May 2024 puts the top-paying metro areas for software developers in a narrow cluster: San Jose–Sunnyvale–Santa Clara at $180,320 median, Seattle at approximately $165,000, and San Francisco–Oakland–Fremont at approximately $161,000. Austin sits at $128,750 and the national median lands at $133,080. The headline spread is dramatic. But what employers actually apply in practice is a different number.

Mercer data cited by SHRM shows that the pay differential between San Francisco and the Puget Sound area runs about 6% in actual employer practice — versus the 13% that national BLS comparisons would imply. The gap between published labor market data and what companies quietly enforce is one of the most poorly understood dynamics in remote compensation. If your employer anchors to national data, you might expect one adjustment. If they anchor to internal bands, you’ll get another.

This disconnect matters most at the $150k+ level, where the split between base salary and total compensation — including equity grants and target bonus — can magnify or shrink a geographic differential substantially depending on how the employer structures each component.

How the BLS Wage Distribution Maps to Remote Scenarios

Four cities illustrate the range, all using BLS OEWS May 2024 data for software developers (SOC 15-1252):

Sources: BLS OEWS May 2024 (national figures confirmed via BLS OOH, bls.gov); metro-level medians sourced from Hakia Software Engineer Salary Guide 2026 (citing BLS OEWS May 2024 data, published February 2026). Seattle and San Francisco figures are approximations from secondary source aggregation; treat as ±$5,000 range estimates. National percentile equivalents are calculated against BLS OEWS May 2024 distribution: 25th percentile $103,050, 50th $133,080, 75th $169,000, 90th $211,450.

A remote developer based in Austin, employed by a San Jose company that applies full location-based pay, could see a base salary anchored near $128,750 — placing them at essentially the national median despite working for a high-paying employer. The same developer, at a company that pays national-rate regardless of location, would target $133,080 to $169,000 depending on level and band positioning. That’s the policy variable that drowns out geography entirely for some workers.

Finluxy Compensation Percentile: What Geographic Adjustment Actually Does to Your Standing

The Finluxy Compensation Percentile measures where a total compensation package lands within BLS wage distribution for the same occupation and MSA. The geographic adjustment question changes this calculation in two distinct ways depending on which location anchors the employer’s pay bands.

Finluxy Compensation Percentile calculations use BLS OEWS May 2024 national distribution: 25th percentile $103,050; 50th percentile $133,080; 75th percentile $169,000; 90th percentile $211,450. Metro-level percentile comparisons for San Jose and Austin are approximations based on the BLS OEWS May 2024 metro medians as reported by Hakia (February 2026, citing BLS OEWS May 2024). These are illustrative scenarios, not actual employer data.

The takeaway from this table is counterintuitive. A developer earning $128,750 in Austin sits at the local median — correctly priced by their employer’s local-market logic — but reads as below the national median. A developer earning $175,000 from a San Jose-anchored employer in Austin is at the 88th percentile nationally yet only around the 50th percentile in the market their employer actually competes in. Neither figure tells the complete story without the other. Anyone benchmarking with BLS salary data needs to run both calculations before concluding they are over- or underpaid.

How Employer Pay Philosophies Split Into Three Models

The SHRM survey data shows 62% of organizations with multi-location workforces apply some form of geographic pay differential. Within that group, 41% apply differentials as a premium or discount to individual pay, and 33% maintain entirely separate base pay structures by location. The remaining 26% use other methods, including metro-area groupings and tiered regional bands.

Three employer models dominate in practice:

Location-based pay: The employer anchors base salary to the employee’s work location. Moving from San Francisco to Austin triggers a downward adjustment. The logic mirrors OPM locality pay methodology — the federal government’s system for 2025 shows San Francisco at a 46.34% locality premium and Washington–Baltimore–Arlington at 33.94%, illustrating the magnitude of variation that a rigorous location-based model would apply. Few private employers use adjustments this large, but the directional logic is the same.

Company-location pay: Base salary reflects the employer’s headquarters or primary office market, applied uniformly. A firm headquartered in New York sets pay against the New York labor market regardless of whether the employee sits in Dallas or Denver. Workers in lower-cost metros benefit; workers in higher-cost metros may find themselves below local market rate — a relevant concern for anyone benchmarking their compensation against local peers.

National-rate pay: The employer pays a single rate regardless of geography, anchored to either the national median or a specific percentile target. Reddit and Zillow both adopted versions of this model, citing cleaner hiring and retention outcomes. Workers in high-cost metros like San Francisco can find this model disadvantageous relative to local competitors; workers in low-cost metros often come out ahead.

Mercer data shared with SHRM illustrates the real-world compression: tech employers adjusting pay between San Francisco and Seattle were applying roughly 6% differentials where pure labor market data would suggest 13%. The pressure to simplify — and to avoid pay disputes — is compressing geographic differentials in practice.

Occupation Matters: Roles With Larger vs. Smaller Geographic Spreads

Software development has an unusually large geographic spread because labor market competition is concentrated in a small number of high-cost MSAs. Other $150k+ roles show narrower geographic variation.

Sources: BLS Occupational Outlook Handbook, May 2024 data (bls.gov). *75th percentile figures for financial analysts and management analysts at May 2024 were not returned in individual OOH profile searches at time of publication; ranges estimated from 50th and 90th percentile anchors and May 2022 OEWS data showing management analyst 75th percentile of $127,480. Treat 75th-percentile cells as unavailable for these two occupations at this publication date. Financial analyst and management analyst 90th percentile figures confirmed via BLS OOH.

For finance roles, the geographic spread is real but narrower than tech. A financial analyst at the 90th percentile in New York earns substantially more than one in a secondary market, but the MSA-level compression is more gradual. Management analysts — a role closer to consulting in function — show the lowest geographic dispersion of the three, reflecting a more distributed labor market.

The Hidden Cost of Remote: What Workers Are Actually Trading Away

A January 2025 NBER working paper by Zoë Cullen (Harvard), Bobak Pakzad-Hurson (Brown), and Ricardo Perez-Truglia (UCLA) — using field experiment data gathered in collaboration with Levels.fyi between May 2023 and December 2024 — found that tech workers are willing to forgo approximately 25% of total compensation for a job that offers remote work instead of requiring five days in-office. The average job offer in the sample was worth $239,000 annually including bonus and equity grant. That implies a remote-work premium valued at roughly $60,000 in total comp.

That figure is three to five times higher than earlier estimates. It also reframes the geographic pay cut question. A developer who relocates from San Francisco to Austin and accepts a 10% location-based pay reduction — roughly $16,000 on a $160,000 base — is not necessarily being exploited. They may be receiving exactly the trade they’d rationally accept: lower pay, lower cost of living, and retained remote flexibility. The problem is when workers accept the location-based cut without retaining any of those compensating factors — the most common scenario being an employer who applies the geographic reduction and then mandates a return to the office.

This is the insight that most coverage of geographic pay misses entirely: the total compensation calculus on remote work is not just base salary. The equity grant component matters enormously here. A $20,000 cut in base salary at a company where RSU grants are also tied to location-adjusted bands compounds into a significantly larger total comp reduction over a four-year vest cycle. Workers focused only on base salary when evaluating geographic pay policies are looking at one input in a multi-variable equation.

Equity and Location: The Compounding Factor

For roles where equity grants constitute 20–40% of total compensation, geographic adjustment policies applied to equity are consequential in a way that base salary adjustments are not. A base salary cut of $15,000 costs $15,000 per year. An equity grant reduction of $15,000 in annualized value — applied to a four-year grant at hire — costs $60,000 in total comp over the vest period, before considering stock appreciation.

Levels.fyi data, which is self-reported and skewed toward senior roles at larger companies, shows senior software engineers at top tech firms earning median total compensation well above $300,000. The geographic delta between what those same companies offer remote engineers in lower-cost MSAs versus their in-office San Jose or Seattle counterparts is not just a base salary story. Valuing RSU grants correctly within the geographic pay framework is essential for anyone comparing offers across employer pay philosophies.

Levels.fyi data is limited by selection bias — it captures self-reporting by engineers who chose to share, and they skew toward higher-paying companies and senior levels. It serves as a directional benchmark for total comp at the high end of the distribution, not a representative sample of all tech workers.

What the Data Means for $150k+ Households

For a household at $150k+ with one or both earners in remote-eligible roles, geographic pay policy is an active financial variable — not background noise. The decision to relocate from a high-cost MSA to a secondary market while maintaining a remote position can produce one of three outcomes depending on employer policy: a net purchasing-power gain (if the employer does not apply location-based adjustments), a roughly neutral outcome (if the adjustment is modest and cost-of-living savings offset it), or a real income loss (if the employer applies a full location differential that exceeds cost savings).

The metro-level salary data for software developers shows that Austin’s median of $128,750 implies roughly 40% greater purchasing power than San Jose’s $180,320 median, once local cost-of-living differentials are applied — but that ratio collapses if your San Jose employer cuts your base salary to Austin-market levels. The net purchasing-power advantage of relocation only materializes for workers at employers using national-rate or company-location pay models.

Before any relocation or remote-work negotiation, the right questions are: Does this employer apply location-based pay? If so, to base salary only, or to the equity grant as well? And does the employer’s geographic tier structure treat Austin as a Tier 2 or Tier 3 market — because that classification drives the differential percentage more than the actual cost-of-living data does. Those three answers determine whether the geographic arbitrage that looks compelling on paper actually holds up in total compensation terms. Anyone comparing their package to BLS benchmarks should use both the national income percentile data and the local MSA wage profile before drawing a conclusion.

The more nuanced version of this analysis applies at the VP and director level. At those seniority bands, geographic compression is often smaller in percentage terms — executives receive fewer downward adjustments than individual contributors — but the absolute dollar figures are larger. The VP and director salary benchmarks by industry show a wider spread in total compensation than base salary alone, which means the equity and bonus components have more leverage in any geographic negotiation. And for roles that cross from tech to non-tech at the same seniority level, the tech-versus-non-tech salary gap compounds the geographic variable in ways that make cross-employer comparison particularly difficult without a consistent methodology.

Households weighing these trade-offs should treat location-based pay policy as a compensation term to be negotiated at offer stage — not a fixed employer constraint. Several large companies have moved policies in both directions in the past two years, and the product manager salary data in particular shows that the same role can carry meaningfully different geographic differentials across companies in the same industry. That variability is negotiating leverage.