Global hiring for AI and machine learning companies

Quick answers

The five questions AI founders ask first.

Why AI companies employ across borders

AI companies are talent intensive, fast moving and international from day one.

The research talent is spread across a few dozen labs. Strong ML researchers come out of university groups in Toronto, Montreal, London, Paris, Amsterdam, Warsaw, Seoul, Tokyo and Bangalore, and most do not want to relocate for a first job.

Visas cannot keep up. A researcher you want to relocate can wait a year for a permit. Employing them where they sit lets the work start this month.

Data lives in specific places. Regulated training data often has to stay in region, and the people cleaning, labelling and evaluating it must be employed there under local rules.

Cost per experiment matters. Research headcount is the largest line after compute. A lower fully loaded cost extends runway without slowing the roadmap.

Customers want a local face. Enterprise buyers in Germany, Japan or Singapore expect a forward deployed engineer in their time zone.

The roles and where they sit

Each market links to its country guide.

Hiring routes compared

The routes differ on speed, whether inventions and weights are protected, equity, and whether the person can pass an access review.

What a global AI hire actually costs

Illustrative planning figures only, not quoted rates and not statutory percentages for any country. Real numbers depend on the market, the salary and the ceilings that apply.

Worked example, illustrative only. A research scientist in a European market on €110,000 gross, with a 20% contribution load and 2% in mandatory benefits, costs roughly €134,000. Add the EOR fee and a workstation and you are planning around €142,000 fully loaded, before equity related contributions in a vesting year.

The five traps that catch AI companies

These are the failures we get called in to fix. All are expensive after a year. Two can cost you a model.

1. Export controls follow the person, not just the shipment

Advanced computing chips, certain model weights and the technical data behind them can be controlled items under US export rules and under parallel regimes elsewhere. Controls are not only about physical shipment. Releasing controlled technology to a person can be treated as an export to that person's country of nationality or residence, wherever they sit. So hiring a researcher in one country, or a researcher of one nationality in another, can change what they may access: the frontier checkpoint, the cluster or the training recipe. Screen nationality and location before the offer, define access tiers, record the decision, and revisit it when the person moves. An EOR gives you the documented employment location that review needs.

2. Employee inventors have statutory rights in several countries

In the United States an assignment clause settles ownership of what an employee invents. Several major research markets do not work that way. Germany's Employee Inventions Act requires the employee to report an invention in writing, gives the employer a fixed window to claim it, and then obliges the employer to pay compensation based on the invention's value. Japan's Patent Act gives employee inventors a right to reasonable benefit even where the contract vests the invention in the company from the start. France and South Korea have their own compensation regimes. Miss the claiming procedure and the invention can sit with the researcher. Ignore the compensation and you inherit a claim years later, usually during diligence. A locally drafted contract with a working claiming process is the fix.

3. Training data and model residency decide who may see what

Training corpora built from customer data, clinical records, financial transactions or European personal data carry rules about where they may be stored and who may access them. GDPR treats an engineer with access to personal data as processing it wherever they sit, which requires a lawful basis, a valid transfer mechanism when data leaves the region and contractual controls flowing down to everyone who touches it. Some sectors and countries add residency rules keeping data in country outright. Weights trained on that data can inherit contractual restrictions of their own. The practical result is an access matrix: which datasets, checkpoints and evaluation sets a person in a given country may open. Build it before the hire, employ the person under local data rules, and put it in the contract.

4. Equity for researchers works differently in every country

AI researchers expect equity and compare offers on it. The problem is that tax treatment shifts with geography. Three questions decide everything. When does tax arise: grant, vest or exercise? What kind of income is it: employment income at marginal rates with contributions attached, or a capital gain? And who collects it, the employee or you through payroll withholding? Where tax lands at vest, a researcher in a private company owes cash on shares they cannot sell, which turns a retention tool into a grievance. Where the gain is employment income, employer contributions can attach and the plan quietly raises payroll cost. The workable answer is one global plan with country sub plans, RSUs where options are punitive, and cash where equity does not work.

5. Scarce talent cannot wait for a visa

The best candidate for your research role is often a national of one country, resident in a second, and wanted in a third. Skilled worker routes exist in most hubs, and some markets run dedicated talent visas for researchers, but even the fast ones take months, need a sponsoring employer with the right licence, and can fail on a quota or a lottery. Meanwhile the candidate has three other offers. Employing the person where they already are, through an EOR, takes the visa off the critical path. The work starts now. If relocation still makes sense in a year, you sponsor it with a track record in hand, and our mobility and immigration team runs the case.

Where to hire, country by country

Every market below has a full country guide covering costs, payroll, leave and termination.

How Dryft works with AI companies

1. Shortlist on total cost and access. Give us the role, seniority and the access it needs. You get a modelled fully loaded cost across three or four countries, with invention and data flags.
2. Pick the route per country. EOR while the hub is small or unproven, your own entity where the team is durable. We say so when the entity wins.
3. Get inventions and confidentiality right in the contract. Local terms carry the claiming procedure, jurisdiction appropriate assignment language and confidentiality covering weights, data and methods.
4. Document the employment for your export review. Location, employer and start date are evidenced so compliance can decide access before day one.
5. Onboard in days. Offer, contract, registrations, benefits, payroll setup and workstation run in parallel.
6. Run payroll monthly. Global payroll, contributions, filings, equity withholding where required, one invoice across every country.
7. Relocate when it makes sense. Our mobility team runs the visa with employment already in place. Recruiting and managed data teams use the same infrastructure.

FAQ

This guide is general information, not legal, tax, export control or immigration advice. Every cost figure here is an illustrative planning range, not a quoted rate for any country. Export classification and access decisions remain your responsibility. Rules differ by jurisdiction and change regularly. Confirm the position for your countries with a qualified adviser before acting. Last reviewed September 2026.