Codio — 2 certified H-1B filings (FY2022–FY2024)
- 1
- Filings · FY2024
- $83k
- Median base · FY2024
- Data Products (Machine Learning) Research Scientist
- Top role · 2 all-time
- MA
- Top state · 1 all-time
Codio filed 1 certified H-1B LCAs in FY2024, with a median base salary of $83k and the middle 50% of offers between $83k and $83k. Across all years it has 2 certified filings since FY2022, most often for Data Products (Machine Learning) Research Scientist in Massachusetts.
How much does Codio 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. $83k median in FY2024. Base pay only — hover a year for its figures.
How has Codio's H-1B hiring changed by year?
1 certified filings in FY2022 → 1 in FY2024. The change column is each year against the one before.
| Fiscal year | Filings | vs prior FY | Median base |
|---|---|---|---|
| FY2024 | 1 | 0% | $83k |
| FY2022 | 1 | — | $100k |
Which wage levels does Codio file?
DOL prevailing-wage levels I–IV approximate experience tiers (I = entry, IV = fully competent). 0% of Codio'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 I100.0%2
What jobs does Codio 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 |
|---|---|---|
| Data Products (Machine Learning) Research Scientist | 2 | $83k |
Where does Codio 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 |
|---|---|---|
| MA | 1 | $100k |
| NY | 1 | $83k |
What has Codio filed recently?
Every certified LCA on record for Codio: job title, worksite, prevailing-wage level and the offered base salary.
| FY | Job title | Base salary |
|---|---|---|
| 2024 | Data Products (Machine Learning) Research Scientist | $83,491 |
| 2022 | Data Products (Machine Learning) Research Scientist | $100,000 |
Common questions about Codio's H-1B filings
- How much does Codio pay H-1B workers?
- Codio's median certified H-1B base salary is $83k (FY2024), from 1 filings. See the full salary distribution and percentiles above.
- How many H-1B workers does Codio sponsor?
- Codio has 2 certified H-1B Labor Condition Applications on record (FY2022–FY2024).
- What jobs does Codio sponsor for H-1B?
- Codio's most-sponsored H-1B roles include Data Products (Machine Learning) Research Scientist.
- Where does Codio hire H-1B workers?
- Codio's top H-1B worksite states are MA, NY.
- 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 (FY2022–FY2024). An LCA is not an approved visa; pay figures are base salary and exclude bonus and equity. Methodology →