Chip Huyen:

When we actively hire, our startup gets 150 - 200 applications each month. I read every single one of them. Sometimes, I’d talk to a candidate and see that what we perceived as their strongest aspects actually weren’t included in their resume. Occasionally, a candidate would tell me that they didn’t expect their resume to still be screened by humans – had they known, they would have written their resume differently.

The resume evaluation process is pretty much a black box for most candidates. And it is so because few hiring managers have publicly discussed this. I thought I should start the conversation.

Chip’s team mostly hires for infra and ML roles, so some of the advice here will be catered towards those positions. Still, if you’re on the job market you’d be well-served by spending 15-20 minutes reading this post and tweaking your resume where applicable.

Chip Huyen:

As machine learning moves towards real-time, streaming technology is becoming increasingly important for data scientists. Like many people coming from a machine learning background, I used to dread streaming. In our recent survey, almost half of the data scientists we asked said they would like to move from batch prediction to online prediction but can’t because streaming is hard, both technically and operationally…

Over the last year, working with a co-founder who’s super deep into streaming, I’ve learned that streaming can be quite intuitive. This post is an attempt to rephrase what I’ve learned.

Chip Huyen:

In the last year, I’ve talked to ~30 companies in different industries about their challenges with real-time machine learning. I’ve also worked with quite a few to find the solutions. This post outlines the solutions for (1) online prediction and (2) continual learning, with step-by-step use cases, considerations, and technologies required for each level.

Chip Huyen has been on both sides of ML-related interviews and has a lot of expertise on the process:

If you’ve picked up this book because you’re interested in working with one of the key emerging technologies of the 2020s but not sure where to start, you’re in the right place. Whether you want to become an ML engineer, a platform engineer, a research scientist, or you want to do ML but don’t yet know the differences among those titles, I hope that this book will give you some useful pointers.