luisfoliv
Remote: preferred, but open to hybrid positions
Willing to relocate: maybe
Technologies: Java, Scala, C++, Python, JavaScript, TypeScript, React, MySQL, PostgreSQL, Apache Spark, and others listed on my CV.
CV: https://docs.google.com/document/d/15lCC_DzHcZuLdU-p6uub_tuB...
LinkedIn: https://linkedin.com/in/luismo
Email: luismilanooliveira at gmail dot com
About me: 7 years of experience, with some solid experience on startups and on creating new and innovative products. I'm looking for positions on any part of the stack but most of my work history has been as a back-end engineer. I've been a major contributor to some very complex and high-performant systems, and I've always been able to push them to a higher level of quality and performance.
I'm familiar with location-sensitive hashing[1], which acts like a dimensionality reducer but, instead of receiving as input the image itself receives a feature vector extracted from it (or something analogous that allows for similarity assessment).
[1] https://en.wikipedia.org/wiki/Locality-sensitive_hashing
LocationSpark: Distributed In-Memory Data Management System for Big Spatial Data
Mapreduce is Good Enough? (2013) [pdf]
Code Is the Easy Part [video]
Teaching Concurrency (2009) [pdf]
If resources are what you were expecting, Coursera used to have a course named "Mining massive datasets" that covers some of the topics I saw you mentioning in your comments (MapReduce, HDFS, PageRank etc). It was ministered by Jure Leskovec, Anand Rajaraman and Jeffrey D. Ullman.
Although the course itself is not available anymore, the video lectures are still on Youtube, and a quick search returned me the following playlist: https://www.youtube.com/playlist?list=PLLssT5z_DsK9JDLcT8T62...
If you want some reading material by the same people, you should also check this book: http://infolab.stanford.edu/~ullman/mmds/book.pdf