Fast paths into high-impact ML engineering roles | Catherine Olsson & Daniel Ziegler
Channel: 80,000 Hours
Date: 31/05/2019

_Originally released November 2018._ After dropping out of a machine learning PhD at Stanford, Daniel Ziegler needed to decide what to do next. He’d always enjoyed building stuff and wanted to shape the development of AI, so he thought a research engineering position at an org dedicated to aligning AI with human interests could be his best option.

He decided to apply to OpenAI, and spent about 6 weeks preparing for the interview before landing the job. His PhD, by contrast, might have taken 6 years. Daniel thinks this highly accelerated career path may be possible for many others.

On this episode Daniel is joined by Catherine Olsson, who has also worked at OpenAI, and left her computational neuroscience PhD to become a research engineer at Google Brain. She and Daniel have this piece of advice for those curious about this career path: just dive in. If you're trying to get good at something, just start doing that thing, and figure out that way what's necessary to be able to do it well.

Catherine has even created a simple step-by-step guide for 80,000 Hours, to make it as easy as possible for others to copy her and Daniel's success:
https://80000hours.org/articles/ml-engineering-career-transition-guide/

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The 80,000 Hours Podcast features unusually in-depth conversations about the world’s most pressing problems and what you can do to solve them.

Learn more, read the summary and find the full transcript on the 80,000 Hours website:
https://80000hours.org/podcast/episodes/olsson-and-ziegler-ml-engineering-and-safety/