Find Remote Engineering Jobs by Salary Band with RemoteOK (2026)

Q: How do I filter for a specific stack within a salary band?

Use RemoteOK's tags array. Filter rows where salary_min meets your floor AND tags contain your target stack tokens. The tags taxonomy is employer-set so it's much cleaner than keyword extraction from descriptions. A query like salary_min >= 150000 AND 'rust' in tags AND 'senior' in tags returns the senior Rust band reliably.

Published April 27, 2026 · 1506 words · For developers

Thirdwatch's RemoteOK Scraper makes salary-band-targeted remote engineering search a single-call workflow at $0.0015 per record — filter by salary floor, stack tags, and seniority, return a clean ranked list. Built for senior remote engineers job-searching, comp-research analysts, and recruiter agencies placing senior remote talent.

Why search remote engineering jobs by salary band

Remote engineering compensation is highly stratified by seniority and stack. According to Levels.fyi's 2024 remote compensation report, the gap between mid-level and staff-level remote engineering pay widened in 2023-2024 as remote-first companies adopted location-agnostic comp bands. For an experienced engineer searching for the next role, sorting by salary band is more useful than sorting by company name or chronological — most strong roles are in the 130-200K USD band, and only 5-10% of weekly remote-engineering postings cross 200K.

The job-to-be-done is structured. A senior remote engineer wants to see only roles at or above their target compensation. A comp-research analyst wants to track salary-band distributions over time across remote-only roles. A recruiter agency placing senior remote talent wants the high-band cohort piped into their candidate-matching workflow. All of these reduce to filter-by-salary plus tag and seniority filtering on RemoteOK's structured output. The Thirdwatch actor returns the data; pandas filters do the rest.

How does this compare to the alternatives?

Three options for salary-band-targeted remote engineering search:

Approach	Cost per 1,000 jobs	Reliability	Setup time	Maintenance
Manual RemoteOK browsing with salary filter	Effectively unbounded job-searcher time	Low (manual filter)	Continuous	Doesn't scale
Levels.fyi paid candidate filtering	$20–$50/month per searcher	High	Hours	Limited to companies in their dataset
Thirdwatch RemoteOK Scraper	$1.50 ($0.0015 × 1,000)	Production-tested	5 minutes	Thirdwatch tracks RemoteOK changes

For active job-searching with deeply-customised filters, the actor + a personal pipeline beats both manual browsing and paid candidate-filtering tools. The RemoteOK Scraper actor page gives you the structured raw feed.

How to find remote engineering jobs by salary band in 4 steps

Step 1: How do I authenticate against Apify?

export APIFY_TOKEN="apify_api_xxxxxxxxxxxxxxxx"

Step 2: How do I pull a broad set of engineering listings?

Use a high-recall keyword and crank maxResults to capture the weekly volume.

import os, requests, pandas as pd

ACTOR = "thirdwatch~remoteok-jobs-scraper"
TOKEN = os.environ["APIFY_TOKEN"]

resp = requests.post(
    f"https://api.apify.com/v2/acts/{ACTOR}/run-sync-get-dataset-items",
    params={"token": TOKEN},
    json={
        "queries": ["engineer", "developer"],
        "maxResults": 2000,
    },
    timeout=900,
)
df = pd.DataFrame(resp.json())
print(f"{len(df)} listings, {df.salary_min.notna().sum()} with salary published")

A 2,000-job pull costs $3 at FREE pricing. Roughly 1,000 will have salary fields populated.

Step 3: How do I filter by salary band, stack, and seniority?

Apply the band, stack, and seniority filters via pandas tag-membership tests.

def has_tag(tags, t):
    return isinstance(tags, list) and t in [x.lower() for x in tags]

bands = {
    "mid (80-130K)": (80000, 130000),
    "senior (130-200K)": (130000, 200000),
    "staff (200K+)": (200000, 999999),
}

for band_name, (lo, hi) in bands.items():
    cohort = df[
        df.salary_min.notna()
        & (df.salary_min >= lo)
        & (df.salary_min < hi)
        & df.tags.apply(lambda t: has_tag(t, "senior") or has_tag(t, "staff")
                                  or has_tag(t, "lead") or has_tag(t, "principal"))
    ]
    print(f"{band_name}: {len(cohort)} listings")
    if len(cohort):
        print(cohort[["title", "company", "salary_min", "salary_max",
                      "tags", "url"]].head(5).to_string(index=False))

For stack-specific filtering, layer an additional tag membership: & df.tags.apply(lambda t: has_tag(t, "rust") or has_tag(t, "go")).

Step 4: How do I set up alerts on the high-band cohort?

Schedule the actor hourly, persist a snapshot, alert on net-new URLs above the band threshold.

import json, pathlib, requests as r

snapshot = pathlib.Path("remoteok-200k-snapshot.json")
prev = set(json.loads(snapshot.read_text())) if snapshot.exists() else set()

high_band = df[
    df.salary_min.notna()
    & (df.salary_min >= 200000)
    & df.tags.apply(lambda t: isinstance(t, list))
]
current = set(high_band.url.tolist())
new_urls = current - prev

for url in new_urls:
    job = high_band[high_band.url == url].iloc[0]
    r.post("https://hooks.slack.com/services/.../...",
           json={"text": (f":moneybag: *{job.title}* at {job.company}: "
                          f"${int(job.salary_min/1000)}K-${int(job.salary_max/1000)}K "
                          f"({', '.join(job.tags[:5])})\n{url}")},
           timeout=10)

snapshot.write_text(json.dumps(list(current)))
print(f"{len(new_urls)} new 200K+ roles forwarded")

Apify's scheduler at 0 * * * * keeps this loop running automatically.

Sample output

A single record with the salary and tag fields highlighted looks like this. Five rows weigh ~10 KB.

{
  "title": "Staff Backend Engineer",
  "company": "GitLab",
  "salary_min": 180000,
  "salary_max": 240000,
  "tags": ["python", "go", "kubernetes", "staff"],
  "location": "Worldwide",
  "description": "We are looking for a Staff Backend Engineer to lead our distributed systems team...",
  "posted_date": "2026-04-08",
  "url": "https://remoteok.com/remote-jobs/123456-staff-backend-engineer-gitlab"
}

salary_min and salary_max are integer USD when published (null on ~50% of listings). tags is a clean array of employer-set strings — much higher signal than keyword extraction. The tag taxonomy includes seniority (staff, senior, lead, principal, junior), stack (python, go, kubernetes, react, etc.), and function (backend, devops, design, marketing) tokens — all of which compose into multi-criteria filters.

Common pitfalls

Three issues bite salary-targeted searches on RemoteOK. Equity-loaded headlines — a posting at salary_max: 400000 from a Series B startup often includes equity in the headline figure rather than pure cash. Trim outliers above 350K when comparing across listings; for the truly high-end (Anthropic, OpenAI, Stripe), Levels.fyi is the better source. Salary nulls — about half of listings have no published salary. For salary-targeted searching, the actor still surfaces these but they don't compete in your sort; treat null salary as "unknown, manually investigate" rather than zero. Tag-vs-title mismatch — a "Senior Software Engineer" in title may not have senior in tags if the employer didn't tag carefully; for highest recall, fall back to title regex if tag membership doesn't return enough rows.

Thirdwatch's actor returns salary_min and salary_max as integer USD when published, and tags always as a list of strings. The pure-HTTP architecture means a 2,000-job pull completes in under five minutes and costs $3 — small enough to run hourly without budget pain even for active high-band alerting. A scheduled hourly snapshot across the 200K+ band typically generates 5-15 alerts per week. A fourth subtle issue worth flagging is that some employers post the same role at multiple salary bands across different boards or locations to test demand at different price points; if you're aggregating cross-source data, treat the same (company, title-norm) pair with materially different salary as legitimate band exploration rather than a duplicate. A fifth note: hourly cadence on the 200K+ band is also useful as a market-pulse indicator — sustained periods with zero new high-band postings (rare but they happen) usually signal a hiring freeze across a major employer cohort.

Related use cases

Frequently asked questions

What's a meaningful salary band for remote engineering search?

Three useful bands: 80-130K USD (mid-level remote, the largest segment), 130-200K USD (senior remote, where most experienced engineers cluster), and 200K+ USD (staff/principal/specialist, the high-end). RemoteOK publishes salary on roughly half its listings; bands above 200K are rare enough that 30+ listings per week is a meaningful sample.

How do I filter for a specific stack within a salary band?

Use RemoteOK's tags array. Filter rows where salary_min meets your floor AND tags contain your target stack tokens. The tags taxonomy is employer-set so it's much cleaner than keyword extraction from descriptions. A query like salary_min >= 150000 AND 'rust' in tags AND 'senior' in tags returns the senior Rust band reliably.

Are RemoteOK salary bands honest?

Mostly. RemoteOK requires employers to publish a band when they pay to post (their boost-the-listing fee mechanism), and the bands are actual cash compensation. Outliers above 400K USD typically include equity-loaded headlines from well-funded startups; for cash-band analysis trim outliers above 350K. Below 50K USD is similarly rare and usually a typo.

How does RemoteOK's salary data compare to Levels.fyi?

Levels.fyi is self-reported by employees post-offer with both base, bonus, and equity broken out. RemoteOK is recruiter-published headline bands at posting time. Levels.fyi is more accurate for total-comp planning, RemoteOK is better for active job search and trend tracking. Use both — Levels.fyi for what to expect once hired, RemoteOK for what is currently open.

Can I get alerts when high-band roles get posted?

Yes. Schedule the actor hourly with a salary-floor filter; persist to a snapshot file; alert on net-new URLs. For the 200K+ USD band where weekly volume is low, hourly cadence is reasonable. For 130-200K, six-hourly is enough. Pair with email or Slack webhook routing to put the alert in front of you.

How do I sort by total compensation rather than just base?

RemoteOK doesn't break out base vs equity vs bonus, so the salary fields represent whatever the employer chose to advertise. For total-comp ranking, sort by salary_max descending and apply a cap (e.g. 350K) to filter equity-loaded headlines. For real total-comp data, cross-reference candidate companies against Levels.fyi or Glassdoor.

Run the RemoteOK Scraper on Apify Store — pay-per-job, free to try, no credit card to test.