jhoon

Stripe - Financial Products | Software Engineers | New York, NY, USA | Full-time | ONSITE

Stripe is the best software platform for running an internet business. We handle billions of dollars every year for millions of businesses around the world. One third of Americans bought something on Stripe in the last year.

Looking for all our open positions in San Francisco, Seattle, Dublin, Singapore, and other locations? Head over to https://stripe.com/jobs.

My NYC-based team is looking for engineers to help us build new financial products for Stripe users. You’ll work on problems that run the gamut from frontend product development, to backend infrastructure, to ML engineering. As you work in each of those areas, you will collaborate with experts in design, infrastructure, and machine learning to build robust, polished products that power the businesses of Stripe users around the world.

Apply here: https://stripe.com/jobs/listing/full-stack-engineer-financia... …or email me directly at (my HN username)+hn@stripe.com

You're correct -- Stripe self-imposed the seven-day delay early in its development.

While we may have chosen the seven-day delay, there are still a number of real challenges in moving to a faster transfer schedule. For example, we need to make sure funds are received from our banking partners in time to transfer them to our users.

(I work at Stripe)

Stripe is trialling faster transfer schedules for US-based users, meaning you'll get your money in two business days instead of waiting one week.

If you'd like to be part of the beta, you can visit https://manage.stripe.com/faster-us-transfers and if you're US-based, you'll automatically be registered to participate in the beta. We're incrementally adding users to the beta, so we'll let you know as soon as you've been added.

(I work at Stripe.)

Your point regarding lost meaning is quite salient. It can be useful and enlightening when a learner reports a measure feature importance (such as in random forest models, http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home....).

When you say "hashing makes the programming a little easier," I think you hit the nail on the head. I'm not trying to improve classification accuracy -- my goal was just to make it as easy as possible to learn on arbitrary structured data.

First, please don't use hashkernel in production. It's a toy designed to showcase how hash kernels can help us (easily) learn on structured data.

VW is faster, smarter, and more featureful. However, it doesn't know how to learn on structured data.

This is a great question. The "unprincipled" part comes in the application of hashkernel. For example, any integer-valued fields encountered in structured data are treated as categorical features (whereas floating-point numbers are treated as continuous features). Of course, it's possible that some of these integer-valued fields should be treated as continuous features. When using techniques in ways they were never intended to be used, like treating continuous features as categorical, many of the assumptions that make something "principled" no longer apply.

Great point! VW was actually an inspiration for this blog post. I don't believe it handles structured data quite the same way as hashkernel, but it does a great job of accepting both categorical and continuous features. We use VW models to classify all sorts of badness on my team at Facebook (http://www.newscientist.com/article/dn21095-inside-facebooks...).