Unlearn.ai, Inc. H-1B wage filings

San Francisco, CA. Filings decided July 2025 to June 2026.

Unlearn.ai, Inc. filed 5 wage applications for H-1B and similar visas from July 2025 to June 2026. The median wage it offered was $155,000, exactly the government's prevailing wage, the legal minimum, for the same jobs.

Filings
5
Positions covered
5
Certified
100.0%
Job titles
3
Worksite cities
3
Filings since FY2022
12

Offered wage since 2022

Unlearn.ai, Inc. made 12 filings across 4 fiscal years since 2022, too few in most years to track the wage year by year. Its busiest full year was fiscal 2022, with 4 filings.

Fiscal years run October to September. Fiscal 2026 is still running: these figures cover October 2025 to June 2026.

Unlearn.ai, Inc. by fiscal year

Fiscal yearFilingsMedian offeredvs year beforeMedian prevailingvs prevailing
2026 (Oct to Jun)5$155,000n/a$148,678at the minimum
20251n/an/an/an/a
20242n/an/an/an/a
20230n/an/an/an/a
20224n/an/an/an/a

Offered wage by job title

Middle half of offered wagesMedian offered wageMedian prevailing wage

Job titleFilingsMedian offeredOffered range and prevailing wagevs prevailing
Applied Machine Learning Scientist2$155,000+38.9%
Clinical Data Scientist2$148,720at the minimum
Senior Applied Machine Learning Scientist1$167,950at the minimum

Where the jobs are

WorksiteFilingsMedian offered
San Francisco, CA3$155,000
Denver, CO1$155,000
San Mateo, CA1$148,720

By visa type: 3 H-1B, 2 H-1B1 (Singapore). By outcome: 5 certified.

Filings by quarter

Oct to Dec 20253
Apr to Jun 20262

Across all employers, filings bunch up in the first half of each year around the annual H-1B lottery, so compare a quarter with the same quarter a year earlier, not with the one before it.

Seniority of the jobs filed

Level I, entry0%
Level II, qualified40%
Level III, experienced40%
Level IV, fully competent20%

The government assigns each job one of four wage levels. A higher level means a higher legal minimum.

What these numbers are

Source: U.S. Department of Labor, OFLC disclosure data. How we clean and count it.

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