A Chinese scientist spent 15 years at Google before moving to Apple and then joining Meta’s Superintelligence team. Now he has made the jump to OpenAI. Circulating alongside this career trajectory is an eye-catching figure: a $140 million pay package.

The Chinese AI Star Who Couldn’t Be Retained Even With a $140 Million Pay Package — What’s His Background?

A Chinese researcher spent 15 years at Google before moving to Apple and then joining Meta’s Superintelligence team. Now he has made the jump to OpenAI. Circulating alongside this career trajectory is an eye-catching figure: a $140 million pay package.

This figure has not been independently verified by authoritative sources. Yet more noteworthy than the rumored compensation is this question: why do Google, Apple, Meta and OpenAI all want the same person?

His name is Ruoming Pang.

Judging only by the roster of companies, his career reads like a fantasy success story. But a closer look reveals he did not simply ride the AI boom by chance. Instead, over more than a decade, he built an unshakable competitive moat of his own.

01 From Top Universities to Tech Giants: 15 Years of Accumulation Creates the Real Edge

Ruoming Pang earned his bachelor’s degree in Computer Science from Shanghai Jiao Tong University. He then pursued graduate studies in the United States, completing a master’s at the University of Southern California before going on to earn his PhD at Princeton University.

This academic path undoubtedly opened doors to elite tech firms.

Yet in the North American job market, a prestigious university background only helps you land more interviews. It does not guarantee you can stay at the core of the industry long-term. What truly set Pang apart from other candidates was the deep technical expertise he built across his 15-year tenure at Google.

During those 15 years, the tech industry cycled through search, mobile internet, cloud computing, deep learning and large language models. Many roles rose to fame then faded away, while countless professionals hopped between tracks chasing the next hot trend.

Pang, by contrast, stayed focused on large-scale computing, distributed training and AI infrastructure.

This work is often invisible to ordinary end users, yet it determines whether models can be trained reliably, run stably, and whether research breakthroughs can be turned into real products for enterprises.

His resume was not built by frequent job hopping. Instead, long-term dedication to a high-impact field naturally gave him far greater career options.

This is a point frequently overlooked in North American job hunting: your first job sets your starting line, but the skills you build over time decide how far you can go.

02 Four Giants, One Constant Core Competency

After leaving Google, Pang joined Apple to work on foundational models. He later moved to Meta’s Superintelligence team to lead AI infrastructure work, and now he has joined OpenAI, continuing to focus on large models and underlying systems.

On the surface, this looks like a sequence of moves from one big tech firm to another. But if you remove the company names, his career arc becomes crystal clear:

To train increasingly complex AI models efficiently and operate them reliably.

Google needed large-scale AI systems. Apple needed to embed model capabilities into consumer products. Meta required infrastructure to support its massive training campaigns. OpenAI also grapples with challenges around compute power, cost and scalable deployment.

The companies and business priorities differ, yet the core technical challenges they aim to solve are fundamentally similar. This is the “transferable skill set” highly valued in North American workplaces.

Many job seekers package themselves around company brands, job levels and project names. Rarely do they pause and ask: what can I take with me if I leave this platform?

Truly competitive engineers are not merely familiar with internal tools or attached to flagship projects. They possess reusable frameworks for solving problems. They know how to break down complex challenges, build systems, and turn technology into stable, deployable outcomes.

A prestigious platform can polish your resume, but only transferable skills keep you valuable no matter where you work.

03 For North American Job Seekers: Build a Clear Career Narrative, Rather Than Chasing Trends

Many international students job hunting in North America struggle with the same anxiety: which roles are easiest to land this year? Which track pays the most? Is it still worth switching into AI now?

These are practical questions. But if your career planning revolves solely around short-term hype, your resume may look packed with experiences while lacking a coherent story that convinces hiring managers.

Pang’s journey offers an alternative mindset.

First, instead of only asking “what is hot right now”, ask whether this field addresses enduring, meaningful problems. Trends come and go, but enterprises’ demand for efficiency, cost control, stability and scalability will remain.

Second, make every experience build on the last. Internships, research and full-time roles should not be disconnected bullet points. Recruiters want to see you tackling harder problems, taking on greater responsibility, and deepening your expertise over time.

Third, craft your own career narrative. North American hiring teams evaluate not just what you have built, but also your motivations, what you have learned, and how your experience prepares you for your next role.

Few people can replicate Pang’s academic and corporate background. Still, we can learn from his approach to career building:

Find a high-impact problem to work on, continuously build portable skills, and make every choice strengthen your position for what comes next.

In the North American tech landscape, real job security never comes from landing a role at a single giant company. It comes from being in demand even after you leave that company.