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Abnormal AI is seeking a Technical Lead (SWE-3) to architect and scale the high-throughput, low-latency infrastructure powering real-time threat detection. This role bridges complex system design and strategic execution, focusing on building a cutting-edge data platform at massive scale that transforms raw data into actionable intelligence for ML models and security researchers.
You will own the architectural blueprint for distributed data pipelines (Spark/Airflow) and cloud-native infrastructure (AWS/EKS). Key responsibilities include designing and maintaining high-throughput detection systems and backend services with low latency and high reliability; leading development of robust data pipelines to supply detection teams with high-fidelity data for ML model training; partnering with Engineering Managers to define technical roadmaps and mentor junior/mid-level engineers; driving best practices in code quality and system design; and leading the team in adopting AI productivity initiatives using tools like Claude, OpenAI, and GitHub Copilot to accelerate development and automate workflows.
You will collaborate cross-functionally with TPMs, Product Managers, Data Scientists, and Security Researchers to translate complex business needs into scalable technical solutions. You will also manage and optimize cloud-native infrastructure on AWS/EKS, ensuring systems are cost-effective, secure, and performant.
Required qualifications: 8+ years of professional experience in production-level backend development with Python or Golang; expert-level proficiency in building and optimizing distributed data pipelines using Spark and Airflow; extensive experience managing and scaling cloud-native applications on AWS (preferred), GCP, or Azure with hands-on container orchestration using EKS; experience leading technical projects, mentoring engineers, and contributing to long-term technical strategy; strong ability to design complex integrations and handle significant throughput/latency challenges; methodical approach to performance debugging and resolving bottlenecks in large-scale systems; strong experience with Postgres or similar relational databases at scale; BS in Computer Science, Applied Sciences, or related engineering field.
Nice-to-have skills include familiarity with React and TypeScript for internal tool visualizations, experience with Databricks or Snowflake data lakehouse architectures, background in threat detection or network security, and advanced degrees in Computer Science or Electrical Engineering.
