Machine Learning Detection Engineer (remote, East/central)

CrowdStrike CrowdStrike · Enterprise · VA +33 · Remote

CrowdStrike is seeking a Machine Learning Detection Engineer to analyze malware and customer detection tickets, focusing on improving the efficacy and reducing false positives of their AI-native cybersecurity platform's machine learning models. The role involves analyzing detection data, investigating binary files, and addressing internal questions about threat detections.

What you'd actually do

  1. Analyze detection data including customer reports to determine which aspects of the machine learning models can be improved
  2. Perform tasks to enable better-management of false positive detections
  3. Analyze binary files to determine their legitimacy
  4. Review current product detections to ensure they are performing to the company standard
  5. Address internal questions and concerns regarding customer threat detections

Skills

Required

  • Exposure and understanding of different types and functionality of malware
  • Experience with reverse engineering malware, or malware operations
  • Knowledge of programming and scripting languages, in particular Python
  • Fundamental understanding of attributes of binary files such as imports/exports and packers
  • Ability to demonstrate practical knowledge of research/collection skills and analytical methods
  • General understanding of threat/risk management and threat/risk assessment
  • Familiarity with various operating systems
  • Ability to break down complex problems into workable components

Nice to have

  • Experience in a security operations center or similar environment responding to incidents
  • A thorough understanding of Windows OS internals and the Windows API
  • Knowledge of MacOS and/or Linux
  • Familiarity with tools used in targeted and criminal cyber-intrusions
  • A background in exploit and vulnerability analysis
  • Knowledge of a variety of programming languages including C, C++, Java, and assembly
  • Experience with threat detections by machine learning

What the JD emphasized

  • experience with detections of potentially malicious behavior by machine learning models is a plus
  • Experience with threat detections by machine learning

Other signals

  • improving detection capability
  • analysis of malware or other threat detections
  • analysis of detection data
  • improving machine learning models
  • managing false positive detections