Staff Software Engineer - Detection Serving & Signals

Abnormal AI is a cybersecurity company focused on protecting the modern workplace from sophisticated threats using AI-native technology. As a Staff Software Engineer in the Detection Team, you will play a critical role in developing cutting-edge technology to identify and thwart sophisticated email and cloud-based attacks. The Detection Division is at the forefront of innovation, building systems that provide world-class security while maintaining high performance and reliability.

Key Responsibilities:

• Lead the architecture, design, and implementation of highly scalable backend services supporting our Detection Engine
• Spearhead critical projects to meet ambitious goals, such as scaling components of our scoring infrastructure by 10x while maintaining or improving performance
• Collaborate closely with ML Engineering teams to gather requirements and drive execution of infrastructure improvements
• Mentor and coach junior engineers through 1-on-1s, pair programming, and high-quality code reviews
• Continuously optimize system performance, reliability, and efficiency to meet growing demand and evolving threat landscape

Requirements:

• 8+ years of professional experience building and scaling data-intensive products
• Extensive experience with real-time, high-throughput & low-latency distributed systems
• Proven ability to maintain 99.99% uptime for services handling 50k+ QPS
• Strong track record of cross-functional collaboration and driving complex projects to completion
• Demonstrated leadership in setting and maintaining high standards for project execution and code quality
• Experience with cloud-native architectures and microservices
• Experience with event-driven architecture such as Kafka, Pub/Sub, etc.

Preferred Qualifications:

• Familiarity with ML systems and distributed technologies (e.g., Python, Golang, Kafka, Redis, Docker, Kubernetes)
• Hands-on experience optimizing high-throughput online systems
• MS or PhD in Computer Science, Electrical Engineering, or a related field
• Familiarity with the cybersecurity industry or fraud detection challenges