Quadsci AI — 2 certified H-1B filings (FY2024–FY2026)
- 1
- Filings · FY2024
- $150k
- Median base · FY2024
- Dataml Engineer
- Top role · 1 all-time
- CA
- Top state · 1 all-time
Quadsci AI filed 1 certified H-1B LCAs in FY2024, with a median base salary of $150k and the middle 50% of offers between $150k and $150k. Across all years it has 2 certified filings since FY2024, most often for Dataml Engineer in California.
How much does Quadsci AI pay H-1B workers?
Certified base salary by fiscal year: each box is the p25–p75 range, the heavy line the median, the whiskers reach p10–p90. $150k median in FY2024. Base pay only — hover a year for its figures.
How has Quadsci AI's H-1B hiring changed by year?
1 certified filings in FY2024 → 1 in FY2026. The change column is each year against the one before.
| Fiscal year | Filings | vs prior FY | Median base |
|---|---|---|---|
| FY2026 | 1 | 0% | $170k |
| FY2024 | 1 | — | $150k |
Which wage levels does Quadsci AI file?
DOL prevailing-wage levels I–IV approximate experience tiers (I = entry, IV = fully competent). 50% of Quadsci AI's filings sit at Level III–IV. A heavy I–II skew reads as entry-level or contractor-style hiring; a heavy IV skew as senior in-house roles — and higher levels earn more lottery entries.
Share of 2 filings with a wage level · all years
- Level II50.0%1
- Level III50.0%1
What jobs does Quadsci AI sponsor for H-1B?
The most-filed job titles across all years (DOL titles, employer pay-grade codes stripped), with base-salary percentiles.
| Job title | Filings | Median base |
|---|---|---|
| Dataml Engineer | 1 | $150k |
| Forward Deployed Engineer | 1 | $170k |
Where does Quadsci AI hire H-1B workers?
Worksite states on the certified filings, all years. Deeper pink = more filings; hover a state for its figures.
| State | Filings | Median base |
|---|---|---|
| CA | 1 | $170k |
| NJ | 1 | $150k |
What has Quadsci AI filed recently?
Every certified LCA on record for Quadsci AI: job title, worksite, prevailing-wage level and the offered base salary.
| FY | Job title | Base salary |
|---|---|---|
| 2026 | Forward Deployed Engineer | $170,000 |
| 2024 | Dataml Engineer | $150,000 |
Common questions about Quadsci AI's H-1B filings
- How much does Quadsci AI pay H-1B workers?
- Quadsci AI's median certified H-1B base salary is $150k (FY2024), from 1 filings. See the full salary distribution and percentiles above.
- How many H-1B workers does Quadsci AI sponsor?
- Quadsci AI has 2 certified H-1B Labor Condition Applications on record (FY2024–FY2026).
- What jobs does Quadsci AI sponsor for H-1B?
- Quadsci AI's most-sponsored H-1B roles include Dataml Engineer, Forward Deployed Engineer.
- Where does Quadsci AI hire H-1B workers?
- Quadsci AI's top H-1B worksite states are CA, NJ.
- Does a certified LCA mean an H-1B visa was approved?
- No. A Labor Condition Application (LCA) is the Department of Labor attestation an employer must have certified before filing an H-1B petition with USCIS. It records the job, worksite and offered wage, but not whether the petition was filed, selected in the lottery, or approved. Filing counts measure intent to sponsor, not headcount.
- What salary do these figures show?
- The base annual wage offered on the certified LCA, converted to a yearly amount. Bonus, equity, overtime and benefits are not included, so total compensation is typically higher. Figures are medians and percentiles over certified filings; wages above $5,000,000 a year are excluded as data-entry errors.
- What do prevailing-wage levels I–IV mean?
- The Department of Labor assigns each LCA an OES wage level from I (entry) to IV (fully competent) for the occupation and area. Under the FY2027 wage-weighted H-1B lottery rule, a higher level earns more selection entries, so an employer's level mix is a proxy for how senior its sponsored roles are.
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Source: U.S. Department of Labor, Office of Foreign Labor Certification — certified LCA disclosure data through June 2026 (FY2024–FY2026). An LCA is not an approved visa; pay figures are base salary and exclude bonus and equity. Methodology →