Are you a Python programmer based in North or South America, interested in large-scale data processing (terabytes per month, petabytes in our archive), and making use of massively-parallel computing architectures, such as those behind Spark and Dask? Or, are you a Machine Learning Engineer in Europe or the UTC to UTC+3 timezones, interested in making use of modern ML techniques atop an open source Python stack? If so, then you should apply for our fully distributed team, since we are hiring for both roles.
Python Data Engineers write code that runs on hundreds of cloud nodes, using best-in-class distributed database technologies like Kafka, Cassandra, and Elasticsearch. Machine Learning Engineers use cutting-edge techniques to move our analytics, recommendation, and natural language processing stack forward; they have built working production systems using word embeddings, topic clustering, and deep learning. Together, members of these two teams power a massive time series analytics engine and content crawl database that offers an elegant user experience to hundreds of enterprise customers. And we do all of this on a team that's small enough to be nimble, but large enough to be dangerous. 15 total engineers, growing to 25 by the end of 2021.
You'd be entering our hiring process early, as we only just kicked off our hiring wave in June, 2021, after Parse.ly's recent acquisition by Automattic -- one of the world's largest fully distributed teams, and one of the biggest champions of open source and open web technologies.