Engineering Manager, Abnormal AI

• 5+ years of software engineering experience, with 2+ years in an engineering management role
• Strong experience designing and operating distributed, data-intensive systems
• Hands-on familiarity with batch processing and/or real-time streaming systems (e.g., Kafka, Spark, Flink, Beam)
• Proven ability to lead complex, high-impact technical initiatives and make sound architectural tradeoffs
• Excellent communication skills and experience working cross-functionally

Desirable

• Abnormal AI is seeking an experienced Engineering Manager to lead our Aggregates Systems team, which owns some of the most mission-critical infrastructure in the company
• These systems sit at the core of our detection architecture, transforming raw behavioral signals into high-quality aggregate datasets that directly power security decisions across all Abnormal products
• The Aggregates Systems operate at extreme scale—processing tens of billions of events per day and approaching one million events per second at peak—and correctness, availability, and timeliness are non-negotiable
• In this role, you will manage and grow a team of engineers responsible for building, operating, and evolving large-scale batch and real-time aggregation systems that Abnormal’s detection, messaging, and customer trust depend on
• You will partner closely with engineering, data science, product, and infrastructure teams to ensure these systems remain reliable, performant, and scalable as the company grows
• Technical & Systems Leadership
• Own the technical direction, reliability, and long-term evolution of aggregation systems spanning both batch and real-time processing
• Guide architectural decisions for distributed data processing, storage, and retrieval systems with strict correctness, latency, and availability requirements
• Ensure aggregation systems consistently meet SLAs for data freshness, accuracy, and uptime across detection and messaging use cases
• Act as a senior technical steward for systems whose failure or inaccuracy would have direct customer and security impact
• People Management & Team Development:
• Manage, mentor, and develop a team of software engineers working across batch and streaming aggregation systems
• Foster a culture of technical excellence, operational ownership, and continuous improvement
• Support career growth through coaching, feedback, and clear performance expectations
• Cross-Functional Collaboration:
• Partner with Detection Engineering, Data Science, Product, and Infrastructure teams to translate detection requirements into scalable aggregation capabilities
• Collaborate with stakeholders to define roadmaps that balance feature delivery with system stability and long-term maintainability
• Serve as a technical and organizational point of accountability for aggregation systems
• Operational Excellence:
• Drive strong operational practices, including monitoring, alerting, incident response, and post-incident analysis
• Improve observability, data quality validation, and resiliency across batch and real-time systems
• Ensure systems scale efficiently as data volume, customer footprint, and use cases expand
• Systems You’ll Own:
• Aggregates System
• In Online systems, the team operates low-latency aggregation systems designed to process high-throughput event data with strict timeliness guarantees
• Composed of event producers, a Kafka-based event stream, and a signals stack including ingestion and storage update services
• Enable fast, flexible retrieval of aggregate values for security and messaging applications
• In Offline systems, the team operates batch processing systems that transform large volumes of raw behavioral signals (e.g., emails and user activity) into comprehensive aggregate datasets
• Operate on scheduled cadences and support long lookback windows for historical analysis and trend detection
• Play a foundational role in determining whether messages are safe or malicious by powering detection logic and models