Tech giants escalate AI hiring battle with multimillion-dollar offers

Meta, Google, Microsoft and other major technology firms are intensifying their search for artificial-intelligence specialists, dangling compensation packages that now reach nine digits for the most sought-after researchers. The extraordinary sums highlight both the soaring cost of developing large language models and the limited number of professionals capable of building them.

Meta, Google and Microsoft lead the bidding war

Meta chief executive Mark Zuckerberg has authorized signing bonuses that sources place near US$100 million for senior OpenAI employees, part of a broader plan to expand the company’s new AI Superintelligence Labs. The strategy also included bringing Scale AI co-founder Alexander Wang on board during a wider US$14 billion investment in that startup.

Google DeepMind pursued a similar strategy when it secured Varun Mohan, co-founder of the coding platform Windsurf, through a package valued at roughly US$2.4 billion. Microsoft, meanwhile, has quietly recruited about two dozen DeepMind veterans for its own AI division. Executives across the sector describe the market for elite talent as “red-hot,” with only a small circle of researchers commanding global attention.

High salaries mirror the cost of building models

Training frontier AI models routinely costs hundreds of millions of dollars, according to Stanford University’s AI Index. OpenAI’s GPT-4 required an estimated US$79 million in 2023, Google’s Gemini 1.0 Ultra approximately US$192 million and Meta’s Llama 3.1-405B close to US$170 million in 2024. Against that backdrop, executives argue that a US$10 million paycheck for a single engineer is relatively modest.

Industry recruiters report parallel escalation in base salaries. In London, senior machine-learning engineers now earn between £140,000 and £300,000, while U.S. figures range from US$175,000 to nearly US$300,000, exclusive of equity and bonuses. A handful of engineers have reportedly received offers exceeding US$250 million, underscoring the premium placed on experience with large language models.

Limited supply intensifies competition

Demand concentrates on two types of professionals: seasoned engineers with years of applied-AI work and newly minted PhDs from top research universities. The number of such experts has grown slowly compared with the explosive interest in generative AI since 2022, creating an imbalance that fuels wage inflation. Analysts say many graduates no longer consider academia, opting instead for immediate corporate roles that provide outsized compensation.

Startups and traditional industries feel the squeeze

While venture-backed startups remain attractive workplaces, founders concede they cannot match the salaries now on offer from Big Tech. Insurance, healthcare and logistics firms face an even wider gap, with smaller budgets and slower procurement cycles. Executives in those sectors warn of a looming “opportunity deficit” if they cannot secure AI expertise to modernize core operations.

Some practitioners still choose younger companies for the chance to influence product direction and hold larger equity stakes. Yet recruiters note that many eventually migrate to the stability—and paychecks—of the industry’s largest players. Until training costs decline substantially, few observers expect compensation patterns to change.

Outlook: salaries tied to model economics

Researchers agree that present wage levels hinge on the multibillion-dollar price tags of cutting-edge models. Should the cost of training fall by an order of magnitude, market rates for engineers would likely retreat. For now, however, organizations willing to invest billions in compute resources appear equally prepared to allocate tens of millions to the human capital required to deploy those systems.

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