Detection Machine Learning Manager

Overview

Abnormal AI is looking for a Machine Learning Engineering Manager to lead the Attack Detection team. At Abnormal, we protect our customers against nefarious adversaries who are constantly evolving their techniques and tactics to outwit and undermine traditional approaches to Security. 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 8% of the Fortune 1000 (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 millisecond latency. The team’s mission is to provide world-class detector efficacy to tackle the ever changing adversarial 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, involving modeling communication patterns to establish enterprise-wide baselines, incorporating these patterns as robust signals, and combining these signals with contextual information to create highly precise systems. Signals are built at multiple levels including message level (e.g., presence of particular phrases), sender level (e.g., frequency of sender) and recipient level (e.g., likelihood of receiving a safe message).

These signals are then combined and utilized to train highly accurate model-based as well as heuristic detectors. Additionally, to continuously adapt to new unseen attacks, the team builds out different stages in our automated model retraining pipelines including data analytics and generation stages, modeling stages, production evaluation stages, and automated deployment stages.

The Engineering Manager (EM) will report to the Senior EM of the Message Detection Team and will lead a team primarily composed of machine learning engineers. The EM will be responsible for managing the execution of the roadmap and deliverables while optimizing both human and system resource utilization. The EM’s success or failure impacts our ability to build and iterate on the detection decisioning system at an extremely high recall, enabling us to respond to current and future attacks.

Preventing such attacks, which cause significant customer workflow disruption, is the core of our business and that makes the success of this team, and its leader, highly impactful.