Software Engineer II - Attack Detection

Abnormal Security is looking for a Software Engineer II 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 20% 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). Additionally to maintain an overall high precise detection system, the team innovates on software systems and processes which can be quickly adapted to solve trends seen in the short term as well as generalize well in the longer term.

This role would also have an opportunity to have a significant impact on the overall charter, direction and roadmap of the team. The Software Engineer II would be involved in understanding the domain of false negatives in the fraud detection domain, and build out the strategy for addressing the most pressing customer problems and execute the associated technical roadmap to continuously operate our detection decisioning system at an extremely high precision.

• Architect, build and deploy backend services and infrastructure that drive a world-class Detection Engine
• Deep inspection and row level data analysis of our false negatives and false positives, and produce feature insights and heuristic detectors to iteratively improve our detection efficacy.
• Design and implement feature extraction pipelines that transform raw email data and behavioural patterns into meaningful, efficient structured signals
• Optimize backend features and services that directly power customer-facing detection capabilities, ensuring high performance and reliability.
• Coach and mentor junior engineers via 1on1s, pair programming, high quality code reviews and design reviews

• 3+ years of professional experience as a hands-on engineer building data-oriented products.
• Proven experience with data analysis and using metrics to answer critical questions about system efficacy and drive development.
• Experience with real-time, online, and/or high-throughput & low-latency distributed systems
• Strong ability to independently debug complex data and system issues using log analytics, metrics, and other signals.
• Works well with other stakeholders - has worked with cross-functional teams to drive projects over the finish-line.
• BS degree in Computer Science, Applied Sciences, Information Systems, or another related engineering field.

• Knowledge of ML systems/products and/or distributed system technologies (feature platform serving systems, ML training and ML serving platforms, etc.)
• Experience working with high-throughput offline systems in Python and/or Go
• MS degree in Computer Science, Electrical Engineering or other related engineering field
• Familiarity with cyber security industry

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