Machine Learning Engineer II
Abnormal Security - Remote - USA
Posted Feb 4, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
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- Mental health support
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- Relocation assistance
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- Childcare support
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- 401(k) match
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Schedule
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- Weekend work
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Application
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Where they hire
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About this role
Machine Learning Engineer II Remote - USA About The Role Abnormal AI is looking for a Machine Learning Engineer to join the Message Detection - Attack Detection team. At Abnormal, we protect our customers against nefarious adversaries who are constantly evolving their techniques and tactics to outwit and undermine the traditional approaches to Security. That's what makes our novel behavioral-based approach so… Abnormal. Abnormal has constantly been named as one of the top cybersecurity startups and our behavioral AI system has helped us win various cybersecurity accolades resulting in being trusted to protect more than 25% of the Fortune 500 ( and ever growing ). In a landscape where a single successful attack can lead to financial losses of millions of dollars, the Attack Detection team plays the central role of building an extremely high recall Detection Engine that can operate on hundreds of millions of messages at milliseconds latency. The Attack Detection team's mission statement is to provide world-class detector efficacy to tackle changing attack landscape using a combination of generalizable and auto trained models as well as specific detectors for high value attack categories. This team is solving a multi-layered detection problem, which involves modeling communication patterns to establish enterprise-wide baselines, incorporating these patterns as robust signals, and combining these signals with contextual information to create extremely precise systems. The team builds discriminative signals at various levels including message level (eg. presence of particular phrases), sender-level (eg.frequency of sender) and recipient level (eg.likelihood of receiving a safe message).
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