ReversingLabs was founded in 2009 with the mission of offering organizations the ultimate in threat detection solutions. In 2017, we were honored to receive the JPMorgan Chase Hall of Innovation Award for our truly unique, automated, and scalable static file analysis, malware classification and malware hunting technologies. Our pioneering technologies, exceptional products, and successful customer deployments also drove a $25 million investment in ReversingLabs, backed by some of the savviest investors in the world. With our center of development excellence located in Zagreb, and offices in the United States and Switzerland, ReversingLabs is poised to achieve rapid growth and deliver groundbreaking innovation in 2019.
Our machine learning solutions are used to identify and classify content during static analysis. From our collection of data, we form and annotate datasets, build models and deploy them to be validated against millions of new files daily. Having so many samples means there are plenty of false positives, conflicting detections, suspicious labels, and other weirdness encountered when working with an endless deluge of file content that the modern civilization produces. Someone needs to take a look at those, so here we are.
This is a data quality job, and that means a lot of hand labeling, tracking dataset changes and automatizing the whole process. Malware and other suspicious files can get pretty quirky and exciting, so there are some “wow” moments and a lot of interesting things to learn from the cybersecurity domain. We work on various problems, with different sample formats, and often semi-automated and use clustering to help filter down the volume. Also, we don’t have a convenient closet to keep you in, so you will have to work together with the team, learn how and why we are doing things the way we are, how our development pipeline works, how our datasets are organized, get familiar with our workflows, take part in planning, and other good stuff.
Technologies that you will use:
- Linux command line tools (grep, cut, xargs, sort …),
- Machine learning libraries: SciKit-Learn, Keras, Tensorflow,
- Clickhouse, Elasticsearch stack.
How to apply: Online at firstname.lastname@example.org until 20.09.2019.