How to Crack Machine learning Interviews at FAANG!
<p>Cracking a machine learning interview at companies like Facebook, Google, Netflix, Snap etc. really comes down to nailing few patterns that these companies look for. In this article, I plan to share my experience interviewing with these companies and also how I went about preparing.</p>
<h1>About me:</h1>
<p>I am currently a Principle Machine learning engineer at Roblox in their Personalization team. Previously I worked at Meta and Airbnb. I have been part of two teams at Meta. I was an early ML engineer in the Misinformation modeling team, working on hoax classification models. After 3 years at Misinformation, I moved to the Instagram Ads ranking team where I was part of the core-optimization team building and improving upon Ads ranking models across all surfaces of Instagram (Feed, Stories, Reels, etc).</p>
<h1>My interview experiences</h1>
<p>I’ve been on the job market for the past two months and attended ~10 onsite interviews and have 6 offers from Google and a few others for ML roles. From among my offers, I decided to join Airbnb in their Relevance product ranking team. This was my first job search attempt since graduating with my Master’s almost 6 years ago and I had to learn the landscape of ML interviews to go about strategizing my preparation.</p>
<p>In this article, I will share tips and tricks that could help you prepare for MLE interviews (L4 and above). I’ve attended interviews at Netflix, Google, Snap, Airbnb, Instacart, Doordash, Nextdoor and I’m going to focus this article based on my experience interviewing with these companies.</p>
<p>Machine learning interviews are quite different from vanilla software engineering interviews and the ecosystem is evolving to make these interviews more calibrated and structured. But here are things that you can broadly expect:</p>
<p><a href="https://medium.com/@reachpriyaa/how-to-crack-machine-learning-interviews-at-faang-78a2882a05c5">Website</a></p>