AbstractAirways

Not anymore. I left years ago.

As an anecdote, right before I left I did my standard self-assessment for performance season. I to my horror I realized that I had accomplished nothing for an entire year. This was largely (I believe...) due to factors outside my control: I did lots of work, but nothing came of it due to delays, procedural slowdown, project cancellations, etc. Instead of reprimanding me for this lack of output, my manager put me up for promotion. I left shortly after this.

"From an H.R. standpoint, this is a nightmare... It completely reverses their image as a desirable employer."

Good. I mentor early career and nontraditional-background engineers, and among this set, Google has acquired a certain reputation. To paraphrase, if you can just pass the interview bar, you'll find yourself in an organization where the pay is amazing, the oversight is minimal, the visa/greencard policy is generous, and the HR processes rarely fire anyone. I routinely got to see people who I would never want to work with get hired there.

If Google becomes less of a target for barely-qualified people who want to rest and vest, I'm all for it.

I tried to scrape them and found they had some pretty robust anti-scraping implemented. Did you have to do anything to overcome it?

Reddit | Machine Learning Engineers | New York (NYC), San Francisco (SF), Remote | Full-time | Onsite preferred, once onsite becomes an option

Reddit Ads is the fuel powering Reddit's explosive growth, and the Optimization and Modeling team is the control panel. We are a team of six engineers and three data scientists with diverse backgrounds and educations dedicated to using machine learning and statistical modeling to improve the ads experience on Reddit.

Topics we've explored to date include multi-armed bandits, supervised machine learning, auction dynamics, traffic simulation, platform economics, as well as the whole range of feature engineering, model training, and low-latency serving required to make things work at scale.

If any of this sounds interesting to you, please reach out to techrecruiting@reddit.com.

You can find the official job posting here: https://boards.greenhouse.io/reddit/jobs/2147755

Reddit | Backend and ML Engineers | Full-Time | New York, relocation offered

The Ad Relevance team is hiring backend and full-stack ML engineers in NYC! Our team is applying machine learning to ad quality, targeting, and relevance over our massive datasets. We're a new team, and there's lots of opportunity for individuals to make a difference.

Our technologies include ML in Spark and TF, low latency serving in Java, and lots and lots of python to fill the cracks. You'd be working on all stages of the ML pipeline, from modeling data science and modeling to implementation and infrastructure.

If you're interested shoot us an email at jobs+hn@reddit.com

I'm the hiring manager on this role, I can take this one.

ML engineers fall between data science and production engineering. They're responsible for doing their own data analysis and experimentation, plus some light engineering to implement their models in production. We also hire backend engineers to handle the more hardcore infrastructure tasks, so we don't expect quite the same insane level of coding ability as some companies do.

Reddit | Multiple Position | Full-Time | New York

Reddit is hiring engineers in ads quality for our brand-new NYC office! We're looking for machine learning and backend engineers, both junior and senior.

The ads quality team is responsible for all the machine learning that powers our ads backend, as well as the inference and ML serving infrastructure.

Our backend is in flux, but we're using: Spark and Scala on AWS for machine learning, as well as BigQuery on GCP for analysis. Our ads serving stack is written in go and runs on AWS.

If you're interested shoot us an email at jobs+hn@reddit.com