No recruiting tool has a filter for "degree earned abroad" — so we compute it. Every education record in the professional-profile graph carries an institution; institutions resolve to countries; a US-based machine-learning engineer whose degree comes from outside the US belongs to the population that lives on employment visas or needs the next one — H-1B pressure today, an O-1 upgrade tomorrow, EB-1A / NIW as seniority grows. This cohort computes that population directly from education and career records and ships it ranked, with verified contacts. It is a recurring series: re-ordered five times by the same buyer.
recurring series · latest batch 2026-07 · MEASURED · Outricher data
Employment-visa practices whose caseload runs on H-1B engineers; O-1 practices upgrading H-1B seniors before the lottery does it for them; and immigration-savvy recruiting firms and staffing agencies that sell visa-friendly pipelines to employers. What it replaces: guessing visa dependence from surnames and hoping — this cohort derives it from education records, evidence attached to every row.
Every education record carries the institution; institutions resolve to countries. A US-based ML engineer whose bachelor's is from abroad is the employment-visa demographic by construction — no self-reporting, no keyword guessing.
→ the population no filter can reachMachine-learning roles detected from titles and career history, not self-tagged skills. The latest funnel: 1M US-based engineering profiles scanned → 312,990 in ML-relevant roles → 44,581 with verified foreign education.
→ 1M → 312,990 → 44,581Years of experience computed from dated positions — the graph holds 5.6 billion of them. The 8–15-year band is where O-1 / EB-1A conversations start replacing H-1B renewals; the cohort ranks toward it.
→ O-1 · EB-1A · NIW windowTwenty real rows from the latest 2,000-lead batch. Big-employer names are shown as-is; personal details are masked on this public page — the full rows, with names and verified emails, ship in the dataset (and a free 10-profile sample is one email away).
| Person | Role | Company | Based in | Education | Experience | |
|---|---|---|---|---|---|---|
| Mert S. | AI Machine Learning Engineer Staff | Lockheed Martin | Austin, Texas, United States | Bachelor's Degree — Ahmet Yesevi Üniversitesi | 17 yrs | m***@live.com |
| Sam R. | Staff Machine Learning Engineer/Data Scientist | Databricks | Cambridge, Massachusetts, United States | Scotch College → Doctor of Philosophy (Ph.D.), Massachusetts Institute of Technology | 6 yrs | s***@gmail.com |
| Charlelie L. | Senior Scientific Machine Learning Engineer | NVIDIA | Santa Clara, California, United States | ENS Paris-Saclay → Doctor of Philosophy - PhD, Toulouse INP | 10 yrs | c***@***.com |
| Kaiyan Z. | Senior Machine Learning Engineer | Salesforce | San Francisco Bay Area | The University of Hong Kong → Master's degree, Columbia University in the City of New York | 6 yrs | k***@***.com |
| Mridul K. | Senior ML Engineer | Uber | San Francisco Bay Area | Master of Technology (M.Tech.) — Indian Institute of Technology, Roorkee | 8 yrs | m***@***.com |
| Zhongmou L. | Staff Machine Learning Engineer | Reddit, Inc. | Greater Seattle Area | Rutgers, The State University of New Jersey-Newark | 4 yrs | l***@yahoo.com |
| Carlos G. | Staff Machine Learning Engineer | Private company · ~109 emp | United States | Instituto Tecnológico y de Estudios Superiores de Monterrey → École Polytechnique Fédérale de Lausanne | 4 yrs | j***@gmail.com |
| Nikhil W. | Member of Technical Staff, Machine Learning Engineer II | VMware | Palo Alto, California, United States | University of Mumbai → University of Southern California | 4 yrs | n***@***.com |
| Simon W. | Senior ML Engineer | Apple | United States | Technical University of Berlin → Creative Destruction Lab | 11 yrs | s***@***.art |
| Siva M. | Senior Machine Learning Engineer | Zoox | San Francisco Bay Area | International Institute of Information Technology → Doctor of Philosophy - PhD, Heidelberg University | 12 yrs | k***@gmail.com |
| Arjun A. | Senior Machine Learning Engineer | Apple | Santa Clara, California, United States | National Institute of Technology Calicut → The University of Texas at Austin | 4 yrs | a***@***.edu |
| Gaurav S. | Senior AI Engineer | The State University of New York | San Francisco Bay Area | Internshala Trainings → Master of Science - MS, University at Buffalo | 5 yrs | k***@gmail.com |
| Sharhad B. | Senior AI and ML Engineer | Apple | New York, New York, United States | FourthBrain | 14 yrs | s***@***.com |
| Patrick H. | Senior Autopilot Machine Learning Engineer | Tesla | Palo Alto, California, United States | Karlsruhe Institute of Technology (KIT) → Doctor of Philosophy - PhD, Technical University of Munich | 10 yrs | h***@***.org |
| Ruixuan(Corey) D. | Senior Machine Learning Engineer | San Francisco Bay Area | Tsinghua University → Ph.D., Washington University in St. Louis | 4 yrs | d***@***.com | |
| Manoj A. | Senior Data Scientist & Machine Learning Engineer | Intel Corporation | Austin, Texas, United States | Institute Of Chemical Technology → Executive MBA, Quantic School of Business and Technology | 5 yrs | m***@gmail.com |
| Varsha S. | Senior Machine Learning Engineer | Adobe | Palo Alto, California, United States | Sri Sivasubramaniya Nadar College Of Engineering → Master of Science (MS), Stanford University | 4 yrs | v***@***.com |
| Anuprit K. | Senior Manager, Machine Learning Engineering | Salesforce | New York, New York, United States | College of Engineering Pune | 5 yrs | a***@gmail.com |
| Naveen K. | Senior Machine Learning Engineer | Private company · ~6 emp | Nashville Metropolitan Area | Vellore Institute of Technology → Master of Science - MS(Thesis), University of Georgia - Franklin College of | 5 yrs | n***@***.com |
| Adam P. | Senior ML Engineer | Apple | San Francisco Bay Area | Gordonstoun → Master's, Machine Learning (Computer Science), UCL | 4 yrs | p***@***.com |
Rows are real, drawn from the latest shipped batch. Large-employer names are shown (a masked engineer at a large company is not identifiable); smaller employers are masked to industry and size. The Education column shows the foreign degree first — where an arrow follows, it traces the classic arc: foreign undergraduate → US graduate school. Masking: surnames reduced to initials, email domains hidden except free providers. Nothing here is generated or blended.
This exact cohort has been re-ordered five times by the same buyer — the strongest quality signal a dataset can have. Every batch ships deduplicated against everything previously delivered: a dedup ledger travels with the client, so a re-order is always fresh rows, never recycled ones. The latest batch: 2,000 leads at 100% email coverage and 129 avg fields per lead, drawn from the 44,581-strong qualified pool.
The honesty boundary
Degree geography is a proxy for visa dependence, not a visa-status record — current status is not observable in any dataset, including ours. The cohort is a ranked propensity pipeline, evidence-tagged, for your intake to qualify: signals, not adjudicated outcomes.
Cross-batch totals are measured on the delivery files themselves: 13,188 rows shipped, 11,761 unique people (every batch deduplicated internally; the ledger keeps re-orders fresh). Deep analytics below are computed on the latest batch of 2,000 — the denominator for every percentage.
Reading: a client who re-orders five times is running this as infrastructure, not an experiment. The latest batch is 96.6% fresh versus everything before it — the dedup ledger works.
Degree ceiling on the batch: PhD 263 (13.2%), master's 1,325 (66.3%) — roughly four in five hold a graduate degree, the arc adjudicators recognize instantly.
Big-tech share 31.1%; half the batch sits inside 20,000+-employee companies. These are employed, senior, paid specialists — not job seekers.
46.3% carry a senior / staff / principal / lead modifier. Geography: California-attributable ≥36.7%, New York ≥11.6%, Washington ≥8.7%, Texas ≥7.8%.
Evidence extracted from the 1,560 enrichment-matched rows (78%) — shares of the full 2,000 are lower bounds: publications 27.6%, awards 32.0%, patents 5.2%. Momentum: 43.7% started their current role within 18 months, median 8 years of experience — exactly the window where a status conversation lands. Email 100% (delivery gate), phone 6%.
Everything above ships as a file you own — and a free 10-profile sample matched to your practice comes first.
A full delivery contains:
Each row is the complete data object — career, education, evidence, contacts, scoring. See what one contact looks like, field by field →
AI outreach agents can draft the first touch straight from these fields — referenced to each person's actual career, so the mail lands with a real person, not a template.
Re-runs are deduplicated against everything previously delivered to you and can be narrowed by seniority band, degree geography or employer profile. Datasets $0.19–0.42 per contact; managed outreach and API delivery available.
Or just write — a consultation is free: we will map this cohort to your practice.
Outricher · Visa Talent Engine · recurring series · latest batch 2026-07 · MEASURED · Outricher data