[amayne]

I started working at a prominent AI company in 2020 with no formal background in AI/ML. I was able to bring outside understanding of language and theory of mind into use with large LLMs and create a role as a prompt engineer. My résumé was basically doing interesting things and helping make their models more useful.

Even though I'm not a formal researcher, I've been able to contribute to research projects and be included in papers because the field is so new.

The most important criteria I look for when I interview applicants is what they have built. Github repos, papers even cool Product Hunt projects can have impact.

[visarga]

> The most important criteria I look for when I interview applicants is what they have built.

How do you know the extent of their contribution? Maybe the project was cool but the applicant only had a marginal participation.

I prefer to ask a few comprehension questions that focus on concepts we use every day on the job, like what's the cost of doubling the sequence length for GPT models, or what is cosine similarity and its applications. They should demonstrate they can operate in this space and have a good grasp on the basics.

[amayne]

I look at their personal GitHub repos (when they're relevant.)