What 34 tech companies say they pay software engineers (from 1,067 job postings)

Colton — Wed, 28 Oct 2026 19:29:09 +0000

US pay-transparency laws mean most tech job postings now include a base-salary range. I collected every open job from 46 well-known companies' career sites on 26 October 2026 and took the software engineer postings that list a range: 1,067 postings at 34 companies.

The numbers

Median midpoint of the posted base-salary range, US software engineer postings (all levels except intern, manager and principal):

Company	Median midpoint	Postings
Anthropic	$402k	83
OpenAI	$356k	146
Perplexity	$312k	38
Replit	$295k	11
Pinterest	$271k	18
Asana	$265k	8
Figma	$264k	9
Ramp	$255k	11
Toast	$251k	16
Robinhood	$250k	19
Stripe	$242k	12
Scale AI	$241k	18
Zoox	$238k	45
Airbnb	$238k	16
Instacart	$236k	7
Reddit	$229k	41
Affirm	$225k	35
Databricks	$222k	92
Roblox	$220k	87
Discord	$220k	15
Brex	$216k	30
Datadog	$216k	11
Samsara	$207k	13
Coinbase	$202k	48
Okta	$196k	19
Chime	$196k	18
Notion	$190k	13
MongoDB	$187k	13
Gusto	$184k	21
Elastic	$172k	12
DoorDash	$169k	36
Palantir	$168k	73
Lyft	$166k	9
Twilio	$156k	9

The overall median across all 1,067 postings is $230k: $235k for senior/staff titles and $219k for the rest.

Three things that surprised me

The AI labs are in a different band. Anthropic's and OpenAI's typical posted midpoints are $100k+ above the middle of the list. A typical OpenAI range is $266k–$445k.
Ranges aren't only posted where the law requires it. Across all of these companies' US jobs (not just engineering), 91% of postings in states without a pay-transparency law (Texas, Ohio, Arizona and others) still showed a range, the same share as in states with one (excluding Carvana, a large employer of hourly staff that mostly doesn't post ranges outside those states). Most tech companies publish ranges everywhere.
Ranges are wide but not absurd. Across all 4,645 US postings with a range, the median range is 1.35× from bottom to top (e.g. $150k–$203k); about one in five is 1.5× or wider.

Caveats

Base salary only. Equity and bonus are excluded, and at many of these companies they're a large share of total pay.
Level mix differs by company. A company hiring mostly senior engineers will look higher. Treat this as "what they're hiring for now", not a like-for-like level comparison.
Location mix. Most postings are in San Francisco, New York and Seattle, where ranges are highest.
Companies with fewer than 5 software engineer postings with a range are left out.

How I collected it

All of these companies host their careers pages on Greenhouse, Lever or Ashby, which serve public job-board JSON. I used an Apify Actor I built, Career Site Jobs Scraper, which takes any Greenhouse, Lever, Ashby, Workday or SmartRecruiters careers URL and returns every open job with its description. Then a few lines of Python: a regex for $X – $Y ranges in the description (Ashby and Lever also return structured salary fields), a title filter for software engineer roles, and a median per company.

import re, statistics
RANGE = re.compile(r"\$\s?\d{1,3}(?:,\d{3})*[kK]?\s*(?:-|–|—|to)\s*\$?\s?\d{1,3}(?:,\d{3})*[kK]?")
SWE = re.compile(r"software engineer|software developer|backend engineer|frontend engineer|full[- ]?stack engineer|member of technical staff", re.I)

def midpoint(text):
    m = RANGE.search(text or "")
    if not m:
        return None
    lo, hi = [float(x.replace(",", "").rstrip("kK")) * (1000 if x[-1] in "kK" else 1)
              for x in re.findall(r"\d{1,3}(?:,\d{3})*[kK]?", m.group(0))]
    return (lo + hi) / 2 if 60_000 <= lo <= hi else None

If you want a company added, name it in the comments and I'll rerun it (it needs a Greenhouse, Lever, Ashby, Workday or SmartRecruiters careers site).

Disclosure: I built the scraper used here. The data comes from the companies' own public job postings.

The IKEA Price Index: the same BILLY costs up to 3x more depending on where you live

Colton — Tue, 27 Oct 2026 01:12:29 +0000

Identical IKEA products, priced in 41 countries, October 2026.

IKEA sells the same products, with the same item numbers, almost everywhere in the world. That makes it a natural price index, a furniture version of The Economist's Big Mac Index. I collected IKEA's own online prices in 41 countries for 35 of its best-known products (BILLY, KALLAX, POÄNG, MALM, LACK, HEMNES, ALEX…), converted them to US dollars and compared each country with the median.

The results

Cheapest: Germany (index 86), the Philippines (86), Romania (89), China (90) and the Netherlands (90).

Most expensive: Mexico (172), Saudi Arabia (159), the United States (128), Norway (126) and the UAE (114).

The United States comes out as the third most expensive of 41 countries, and that understates it: US prices are shown before sales tax, while European prices include VAT of 19–27%.

Same product, very different prices

Product	Cheapest	Most expensive	US
KALLAX shelf unit, white	Romania $54	Mexico $150	$80
POÄNG armchair, birch/Knisa beige	Romania $63	Mexico $161	$149
ALEX drawer unit, white	Poland $61	Saudi Arabia $172	$95
HEMNES daybed with 3 drawers	Norway $257	Mexico $803	$449
FRIHETEN sleeper sectional	Thailand $355	United States $999	$999
LACK wall shelf, white	Germany $4.47	Australia $11.24	$7.99

Method

Products: 35 items sold in at least 30 of the 41 countries, matched by IKEA's global item number. Matching by item number means the same size and color, not just the same product family.
Prices: the price shown on IKEA's website in each country on 25 October 2026, converted to USD at that day's exchange rates.
Index: for each item, every country's price divided by the median price across countries. A country's index is the median of those ratios × 100, so 100 means "typical", 128 means 28% above typical.
Caveats: prices include VAT in Europe and most of Asia but exclude sales tax in the US and Canada; delivery costs, promotions and in-store prices aren't included; exchange rates move daily.

Reproduce it

The data was collected with the IKEA Products & Prices Scraper, which I built on Apify. Pick countries and products, and it returns every product's local price plus a cross-country comparison in USD:

{
  "queries": ["billy", "kallax", "poang"],
  "countries": ["US", "DE", "GB", "MX", "JP", "SE"],
  "comparePrices": true
}

Each comparison row lists the cheapest and most expensive country and the spread:

{
  "type": "price-comparison",
  "name": "KALLAX",
  "productType": "Shelf unit",
  "cheapestCountry": "RO",
  "cheapestPriceUsd": 54.24,
  "mostExpensiveCountry": "MX",
  "mostExpensivePriceUsd": 149.82,
  "spreadPct": 176.2
}

I'm the author of the scraper. The index uses only IKEA's public online prices.

DEV Community: Colton