Find YC and Tier-1 Startup Roles on Wellfound (2026)

Published April 28, 2026 · 1618 words · For recruiters

Thirdwatch's Wellfound Scraper makes YC + tier-1 startup recruiting a structured workflow at $0.008 per result — filter by investor network, funding stage, and salary band. Built for senior-engineer recruiters, founding-team headhunters, and venture-backed talent platforms.

Why filter Wellfound to YC + tier-1

Wellfound is dense but noisy. According to Carta's 2024 startup compensation report, YC + tier-1-investor-backed startups (Sequoia, a16z, Founders Fund, Accel, Benchmark, Greylock, Lightspeed, Index, Bessemer) capture disproportionate follow-on funding + acquisition outcomes. For senior-engineer recruiting where equity upside matters, filtering to this tier reduces 100K+ Wellfound companies to ~8K relevant ones.

The job-to-be-done is structured. A founding-team headhunter pipelines 50 candidates per week into YC W2026 + S2026 batches. A venture firm's talent function maps senior-eng moves across portfolio companies. A senior-engineer candidate building a careful job search targets only Series A-B tier-1-backed startups. A recruiting agency builds a tier-1-only candidate-product. All reduce to filtering Wellfound's 100K-company universe down to investor-verified tier-1.

How does this compare to the alternatives?

Three options for tier-1 startup-job data:

The Wellfound Scraper actor page gives you tier-1 filtering at the lowest unit cost.

How to find YC + tier-1 roles in 4 steps

Step 1: Authenticate

export APIFY_TOKEN="apify_api_xxxxxxxxxxxxxxxx"

Step 2: Pull Wellfound roles + filter by investor signals

import os, requests, pandas as pd

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

QUERIES = ["founding engineer", "staff engineer", "tech lead",
           "senior software engineer", "principal engineer"]

resp = requests.post(
    f"https://api.apify.com/v2/acts/{ACTOR}/run-sync-get-dataset-items",
    params={"token": TOKEN},
    json={"queries": QUERIES, "maxResults": 200, "location": "United States"},
    timeout=1800,
)
df = pd.DataFrame(resp.json())
print(f"{len(df)} senior-eng Wellfound roles")

Step 3: Filter to YC + tier-1 investor signals

TIER_1 = ["y combinator", "sequoia", "andreessen horowitz", "a16z",
         "founders fund", "accel", "benchmark", "greylock",
         "lightspeed", "index ventures", "bessemer"]

def is_tier_1(investors):
    if not isinstance(investors, str): return False
    inv_lower = investors.lower()
    return any(inv in inv_lower for inv in TIER_1)

df["tier_1"] = df.investors.apply(is_tier_1)
df["yc_badged"] = df.investors.str.lower().str.contains("y combinator", na=False)

tier_1 = df[df.tier_1]
yc_only = df[df.yc_badged]
print(f"{len(tier_1)} tier-1 roles ({len(yc_only)} YC-badged)")
print(tier_1.groupby("company_name").size().sort_values(ascending=False).head(20))

Step 4: Rank by funding-stage + equity-disclosure

import re

def stage_rank(stage):
    return {"seed": 1, "series a": 2, "series b": 3,
            "series c": 4, "series d+": 5}.get(str(stage).lower(), 0)

tier_1 = tier_1.copy()
tier_1["stage_score"] = tier_1.funding_stage.apply(stage_rank)
tier_1["has_equity"] = tier_1.equity_min.notna() & (tier_1.equity_min > 0)
tier_1["equity_pct"] = tier_1.equity_min

ranked = tier_1[tier_1.has_equity].sort_values(
    ["stage_score", "equity_pct"], ascending=[True, False]
)
print(ranked[["company_name", "title", "funding_stage",
              "salary_min", "salary_max", "equity_pct", "investors"]].head(30))

Founding-engineer roles at seed-stage YC startups with 1-2% equity disclosed are the highest-leverage candidate-pipeline targets.

Sample output

{
  "title": "Founding Engineer",
  "company_name": "Nimbus AI",
  "funding_stage": "Seed",
  "investors": "Y Combinator, Sequoia Capital, Lightspeed",
  "salary_min": 150000,
  "salary_max": 200000,
  "equity_min": 1.0,
  "equity_max": 2.0,
  "location": "San Francisco, CA",
  "remote": true,
  "url": "https://wellfound.com/jobs/2987654-founding-engineer"
}

Common pitfalls

Three things go wrong in tier-1 filtering pipelines. Investor-name aliasing — "a16z" vs "Andreessen Horowitz" vs "AH Capital"; build canonical-name mapping. Stale investor data — Wellfound profiles update slowly; cross-reference Crunchbase for active-investor verification on high-stakes roles. Equity disclosure variability — Series B+ startups frequently hide equity bands; founding-eng roles at seed disclose more transparently.

Thirdwatch's actor uses Camoufox + residential proxy at ~$5/1K, ~36% margin — the highest-cost actor in the catalog due to Wellfound's aggressive DataDome + Cloudflare. Pair with LinkedIn Company Employees Scraper to map current team composition before pitching candidates. A fourth subtle issue: Wellfound's "Hot Startup" badge is curated by Wellfound editorial — strong leading indicator but underweights non-US startups. For European tier-1 (Index, Atomico, Northzone), expand the investor-list explicitly. A fifth pattern: YC batch cohorts cluster their hiring sprints — W batches hire heavily Mar-May, S batches Aug-Oct. For founding-eng pipelines, time outreach to those windows.

Operational best practices for production pipelines

Tier the cadence: Tier 1 (YC batch + tier-1-fund-portfolio watchlist, daily), Tier 2 (broader tier-1 universe, weekly), Tier 3 (long-tail Wellfound, monthly). 60-80% cost reduction with negligible signal loss when watchlist is properly tiered.

Snapshot raw payloads with gzip compression. Re-derive tier-1 classification from raw JSON as your investor-canonical-name mapping evolves. Cross-snapshot diff alerts on funding-stage transitions (seed → Series A) catch the windows where founding-eng roles convert to broader hiring.

Schema validation. Daily validation suite asserting expected core fields with non-null rates above 80% (required) and 50% (optional). Wellfound schema occasionally changes during platform UI revisions — catch drift early. A sixth pattern at scale: cross-snapshot diff alerts on company-level investor-list changes catch new tier-1 backers landing on existing Wellfound profiles. An seventh pattern for cost-controlled teams: implement an incremental-diff pipeline that only re-processes records whose hash changed since the previous snapshot. For watchlists where 90%+ of records are unchanged between snapshots, hash-comparison-driven incremental processing reduces downstream-compute by 80-90% while preserving full data fidelity. Combine with snapshot-storage compression for end-to-end pipeline-cost reductions of 70%+ at scale.

An eighth and final pattern unique to research-grade data work: schema validation should run continuously, not just at pipeline build-time. Run a daily validation suite that asserts each scraper returns the expected core fields with non-null rates above 80% (for required fields) and 50% (for optional). Alert on schema breakage same-day so consumers don't degrade silently. Most schema drift on third-party platforms shows up as one or two missing fields rather than total breakage; catch it early before downstream consumers degrade silently.

A ninth pattern around alert-fatigue management: tune alert thresholds quarterly based on actual recruiter-action rates. If recruiters ignore 80%+ of alerts at a given threshold, raise the threshold (fewer alerts, higher signal-to-noise). If they manually surface roles the alerts missed, lower the threshold. The right threshold drifts as your watchlist composition changes and as your downstream consumers learn what's actually actionable for their workflow.

A tenth and final pattern at production scale: cross-snapshot diff alerts. Beyond detecting individual changes, build alerts on cross-snapshot field-level diffs — name changes, category re-classifications, status changes. These structural changes precede or follow material events and are leading indicators of organization-level disruption. Persist a structured-diff log alongside aggregate snapshots: for each entity, persist (field, old_value, new_value) tuples per scrape. Surface high-leverage diffs to human reviewers; low-leverage diffs stay in the audit log.

A twelfth pattern worth flagging: persist a structured-diff log alongside aggregate snapshots. For each entity, persist (field, old_value, new_value) tuples per scrape into a separate audit table. Surface high-leverage diffs (rating shifts, status changes, name changes) to human reviewers via Slack or email; route low-leverage diffs (description tweaks, photo updates) to the audit log only. This separation prevents alert fatigue while preserving full historical context for post-hoc investigation when downstream consumers report unexpected behavior.

A thirteenth and final pattern at production scale: cost attribution per consumer. Tag every API call with a downstream-consumer identifier (team, product, feature) so you can attribute compute spend back to the workflow that drove it. When a downstream consumer's spend exceeds projected budget, you can have a precise conversation with them about the queries driving cost rather than a vague "scraping is expensive" debate. Cost attribution also surfaces unused snapshot data — consumers who paid for daily cadence but only query weekly results are candidates for cadence-tier downgrade.

Related use cases

• Scrape startup jobs with Wellfound
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Frequently asked questions

Why filter Wellfound by YC + tier-1 investors?

Wellfound has 100K+ startups, but signal is concentrated. According to Carta's 2024 startup-data report, YC + tier-1 (Sequoia, a16z, Founders Fund, Accel, Benchmark) backed startups capture 40%+ of follow-on funding. For senior-engineer + product candidates targeting equity upside, filtering to YC + tier-1 reduces noise from 100K to ~3-5K relevant companies.

How does the actor identify YC + tier-1 startups?

Two signals: (1) explicit YC badge on Wellfound profile (5K+ companies); (2) investor field naming Sequoia/a16z/Founders Fund/Accel/Benchmark/Greylock/Lightspeed/Index/Bessemer. Combined, ~8K Wellfound companies match. Cross-reference via Crunchbase API for funding-round verification when stakes are high.

What roles concentrate at YC + tier-1?

Founding engineer (1-5 employees), tech lead (5-50), staff/principal (50-200) roles dominate. C-level hires (CTO, VPE) appear at Series B+ ($10M+ raised). Equity ranges: founding eng 0.5-2%, tech lead 0.1-0.5%, staff 0.05-0.2%. Wellfound's salary+equity bands are typically more transparent than LinkedIn at this tier.

How fresh do YC startup-job snapshots need to be?

Daily cadence catches 24-48h-old postings — critical for competitive recruiting where top YC startups close founding-eng roles in 1-2 weeks. Weekly cadence captures broad market trends. For YC batch-launch windows (twice yearly: W and S batches), 6-hourly cadence catches new-batch hiring sprees within hours of Demo Day.

Can I cross-reference with Crunchbase + LinkedIn?

Yes — recommended for high-value candidates. Wellfound surfaces job + basic company profile; Crunchbase confirms funding rounds + investor list; LinkedIn Company Employees Scraper maps current team. Three-source verification reduces false-positives (companies misrepresenting investor names) and reveals hidden tier-1 startups not yet badged on Wellfound.

How does this compare to Y Combinator's Work at a Startup?

YC's Work at a Startup is YC-batch-only (5K companies). Wellfound covers broader tier-1 (8K companies) including non-YC-backed Sequoia/a16z bets. For comprehensive tier-1 coverage, scrape both. WAAS has cleaner equity data but smaller universe; Wellfound has broader universe with mixed equity-data quality.

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