From OpenAI to Meta: An Insider's Guide to Landing Top AI Research Positions

Prakhar Agarwal, currently working as an applied researcher at Meta Superintelligence Labs, reveals there exists a clear pathway for individuals aspiring to secure positions at premier artificial intelligence laboratories worldwide.

Agarwal, who previously worked at OpenAI, emphasizes that success in the AI research field fundamentally depends on extensively utilizing AI models, identifying their limitations, and gaining practical experience.

"You're required to spot gaps and solve those problems. Once hired, you're essentially thrown into challenging situations. You must define your own problems and develop solutions," Agarwal explained in his conversation with Business Insider.

He notes that interviews conducted at leading AI laboratories primarily assess a candidate's comprehension of large language model (LLM) fundamentals and their capability to navigate ambiguous, abstract problem domains.

"Having a PhD is beneficial," Agarwal stated. "However, if you can demonstrate similar abilities through alternative means, such as startup experience or developing significant software, that's sufficient to make your resume stand out."

He further emphasized that identifying weaknesses in AI systems represents one of the most valuable skills researchers can possess. "The ability to detect limitations in AI models is truly one of the most crucial qualities all these companies are seeking," he remarked.

Before joining Meta, Agarwal's career journey included positions at Apple and OpenAI. "I began my professional career at Apple in 2020. After five years there, I moved to the OpenAI API team. I joined Meta Superintelligence Labs this summer when many professionals were making similar transitions," he shared.

A University of Washington graduate, Agarwal revealed he didn't need to formally apply for positions at major AI companies. "OpenAI, Meta, and several other companies initiated contact with me, eliminating the need for explicit applications," he added.

Agarwal advises aspiring AI professionals to acquire practical experience and concentrate on problem-solving rather than theoretical knowledge.

"I strongly recommend people get hands-on experience working on actual problems and solutions," he stated. "This approach teaches you what doesn't work and helps develop the intuition that distinguishes you during interviews."

He also highlighted the collaborative and open nature of the AI community, encouraging learners to connect with others in the field.