CENSYS

Senior Machine Learning Engineer

Remote, US$151,000-$206,000Posted 13 days ago

Job Description

### Company Background

Censys' mission is to be the one place to understand everything on the internet. Frustrated by the lack of trustworthy Internet intelligence, we set out to create the industry's most comprehensive, accurate, and up-to-date map of the Internet. Today, Censys delivers real-time Internet intelligence and actionable threat insights to global governments, over 50% of the Fortune 500, and leading threat intelligence providers worldwide.

Censys is building the most credible, robust map of the Internet through IP scanning, DNS lookups, web crawling, and the ingestion of millions of certificates. Censys was founded by security researchers who are passionate about developing technology that provides anyone the power to fully understand their digital risk and exposure. Individuals and enterprises–and anyone in between–can harness this power to discover new information and insight as the Internet, IoT and Cloud evolve. We are a true security startup with midwestern roots and we believe that a map of the Internet, or a map of your organization's assets can quickly help guide organizations to the answers they need to protect themselves from vulnerability and risk.

Location

This position is remote within the United States.

### Role Summary:

We're looking to hire a Senior Machine Learning Engineer to build models and data-driven systems that help classify, label, and enrich vast amounts of Internet data, providing direct value to customers and other parts of our organization. Censys operates distributed infrastructure for Internet-wide scanning and you will help us continue our mission to transform raw Internet telemetry into high-quality datasets, classifications, and insights about the Internet at large.

At Censys, we believe in working iteratively, while keeping the big picture in mind. We're expanding our data platform to enable future products and features that make the Internet more explainable by adding

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