Sr. Machine Learning Engineer

Company: Abnormal Security
Location: Not Specified, Not Specified, United States
Type: Full-time
Posted: 31.AUG.2021
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Summary

Senior Staff ML Systems Engineer - Message Detection The Opportunity Abnormal Security is defining the next generation of email security d...

Description

Senior Staff ML Systems Engineer - Message Detection

The Opportunity

Abnormal Security is defining the next generation of email security defense. Spam is a solved problem, and phishing is nearly solved as well. But attackers don't stop there - they hijack and take over conversations, threaten businesses, impersonate VIPs, send fake invoices, and steal many millions of dollars every year. Here's where Abnormal Security comes in: we're laser focused on solving these problems for our customers. Our enterprise customers love us because we consistently detect and stop what everyone else in the market can't -- advanced attacks that have never been seen before -- and we do so with beautiful user interfaces and best-in-industry customer support.

Our platform uses machine learning and artificial intelligence to baseline communication content, user identity, and behavioral signals in real-time and at-scale in order to detect the abnormalities of email attacks.

Who you are:

  • As an engineering leader on the detection team, you will be key to making our engineering org effective at stopping attacks and continuously improving our ML system.
  • You have experience working on spam or anti-fraud systems, recognize how difficult the problem is, and want the challenge of stopping the most advanced attacks out there.
  • You can combine machine learning and systems to build an end-to-end solution that can continuously improve and adapt to new attacks.
  • You can debug datasets and identify which machine learning models, natural language processing, and modeling techniques work well to solve our problems.
  • You can design the overall system that combines machine learning decisions together.

What you'll do:

  • Own the delivery on technical solution and roadmap to stop advanced email attacks in their tracks.
  • Build ML systems that deliver on a very high precision solution in an email environment
  • Architect systems to deal with quickly-evolving, zero-day adversarial attacks
  • Understand and propose solutions to stop advanced attacks that include content analysis and understanding, behavioral analysis, web crawling, deep attachment inspection, and can deal with the multi-dimensional aspect of email attacks.
  • Architect systems that will enable 15+ other engineers to work effectively together
  • Mentor and work with junior engineers to build future leaders on the team

Experience you'll need:

  • 2+ years working in trust-and-safety, fraud detection, anti-spam, or similar field
  • 4+ years working on machine learning systems and teams
  • 7+ years hands-on experience in engineering roles, preferably building ML systems or products

More About Abnormal Security:

Abnormal Security is defining the next generation of email security defense. Our platform uses machine learning and artificial intelligence to baseline communication content, user identity, and behavioral signals in real-time and at-scale in order to detect the abnormalities of email attacks. Customers love us because we consistently detect and stop what everyone else in the market can't -- advanced attacks that have never been seen before -- and we do so with beautiful user interfaces and best-in-industry customer support.

Our veteran team has built some of the most enduring machine learning platforms at leading companies including Google, Twitter, Pinterest, Amazon, Microsoft, and Expanse. We are located in San Francisco, CA, New York, NY and Lehi, UT.

Our company is growing - we're on the Forbes AI 50 , selected as a Gartner 2020 Cool Vendor , and our customer base includes multiple Fortune 500 companies.

Abnormal Security is committed to creating a diverse work environment. All qualified applicants will receive consideration without regard to race, religion, gender, gender identity, sexual orientation, national origin, genetics, disability, age, or veteran status.

- provided by Dice

 
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