Shiroyagi Corporation (https://shiroyagi.co.jp) | Software Engineer | Tokyo, Japan | Full time | Onsite | Visa

We are developing Web API services utilizing machine learning algorithms. We start to develop our next product which will be built on top of a distributed system combining online and offline algorithms through lambda architecture.

We are currently a small team (less than 10 people) so that we need talented engineers to tackle challenging problems.

We are looking for engineers who are good at some of followings:

- Distributed System

- Machine Learning

- Recommender System

- Dev Ops

- Full Stack

Japanese proficiency is a plus, but not mandatory.

If interested, please send an email at kazuaki.tanida@shiroyagi.co.jp

Last year, I have developed a JS library SM.js (https://github.com/slaypni/SM-15) which is based on the algorithm used in the latest SuperMemo.

I have also developed a web service (http://slaypni.github.io/flashward/) for memorizing Foreign words using the library. Through the experience of using it by myself, I felt that spaced repetition helped me to remember many words for learning English.

I am feeling that most (free) SRS are using relatively simple algorithms (for example, Anki is based on SM-2 which is developed in the late1980s). Although I am not sure SM-15 is better enough, there could be some room to improve it. It is nice if the library was helpful for some developers who create new SRS.

Newer isn't necessarily better with SuperMemo algorithms. They introduced tremendous complexity without strong evidence of increased effectiveness, last I looked into it - it seems to create more corner cases to manage, if anything.

I'd be interested in materials I may be missing.