"I started my career at Apple in 2020. I spent five years there, then moved to OpenAI in the OpenAI API team. I moved to Meta Superintelligence Labs this summer when a lot of folks were making the shift. I was in graduate school at the University of Washington, specializing in machine learning, when I applied to Apple. Later, OpenAI, Meta, and a bunch of other companies began reaching out, so I didn't have to explicitly apply for any of those."
"These roles are very high autonomy. You don't have a traditional setup and hierarchy. Your role involves identifying a gap, then going to solve that problem. It's up to you to prioritize what is the right thing to address in the limited time and resources that you have access to. Once you're in, you're pretty much thrown in the deep end. You define your own problems and try to come up with solutions."
Prakhar Agarwal began his career at Apple in 2020, later joining OpenAI's API team and moving to Meta Superintelligence Labs. He completed graduate studies in machine learning at the University of Washington. Industry outreach increased after entering the field, reducing the need for active applications. Senior experience matters due to limited positions and high autonomy in roles. Researchers must identify gaps, prioritize problems, and propose solutions independently. Interviews assess familiarity with LLM nomenclature and practical, job-related coding skills through scenario-based tasks. Frequent, hands-on use of models, spotting limitations, and learning beyond coursework are key strategies for securing top AI lab roles.
Read at Business Insider
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