# Software Engineer II - ADEX **Company**: Abnormal **Location**: Bangalore **Work arrangement**: hybrid **Experience**: mid **Job type**: full-time **Category**: Engineering **Industry**: Technology **Apply**: https://job-boards.greenhouse.io/abnormalsecurity/jobs/7774637003?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_9edcc54d-aac ## Description Enterprises of all sizes trust Abnormal's AI-native security products to stop cybercrime and protect critical communications, identities, and infrastructure in the cloud. As a Software Engineer II on the ADEX Team, you are a highly capable detection feature owner: you take a detection problem, come up with an idea, design a technical approach, and drive it end-to-end, from design and implementation through launch, operation, and continuous improvement. The Advanced Detection of EXtended threats (ADEX) is an attack-detection team inside Abnormal's Detection org. We own several of the highest-visibility detection surfaces at the company, spanning attachment-based attacks, fraud, and impersonation. ## Responsibilities - Design, build, and operate detection that is core to Abnormal's products, from initial design through rollout, monitoring, and ongoing maintenance. - Own detection projects end-to-end, including those that begin with a degree of ambiguity: scope loosely defined problems, identify risks, define milestones, and deliver reliably. - Analyze attacks that get through. Pull and study missed-attack data, read the messages the way an attacker and an analyst would, identify the underlying pattern, and translate it into detection enhancements or entirely new detection systems. - Write and tune detection logic using scored signals and attributes, add new signals across the pipeline, and drive changes to launch with a strong focus on minimizing false positives. - Build and evaluate LLM-based detection agents, and measure precision and recall rigorously with our evaluation tooling. - Surface your detections as reusable intelligence that other products and teams across the platform can consume. - Participate in the on-call rotation for your detection surfaces, debug and resolve customer escalations, and feed learnings back into design, observability, and runbooks across regions. - Leverage AI as a core part of your development loop for code, tests, data analysis, experiments, and documentation, while maintaining strong engineering judgment and validation practices. - Contribute to team health and culture by documenting heavily, sharing learnings, and giving thoughtful feedback in code and design reviews. ## Requirements - 3+ years of professional software engineering experience, with a track record of shipping and operating production systems. - Strong software engineering fundamentals: data structures, algorithms, system design basics, testing, debugging, and clean, maintainable code. - Strong Python proficiency and comfort learning new languages and frameworks as needed. - Solid data-analysis instincts. You are comfortable with SQL and reasoning over large datasets to find signals in noise. - A detection or adversarial mindset. You enjoy thinking like an attacker, reading real attack samples, and asking, "How would I get past this?" - Genuine fluency with AI-native development. You already use AI coding agents in your daily work and are excited to build LLM-powered detection, not just consume AI tools. - Demonstrated ability to own projects that carry some initial ambiguity: clarify and scope loosely defined requirements, make tradeoffs explicit, deliver on time, and communicate status clearly. - Excellent written and verbal communication, especially in remote, distributed teams. - A strong growth mindset and sense of ownership. ## Nice to Have - Experience with distributed systems, high-throughput pipelines, or large-scale data stores (e.g., PostgreSQL, DynamoDB, Redis, RocksDB, Kafka, Spark, OpenSearch/Elasticsearch). - Background in security, threat detection, anti-abuse, fraud detection, or trust and safety, particularly systems processing high volumes of email or communication data. - Experience with ML or LLM evaluation: precision/recall tradeoffs, eval harnesses, prompt iteration. - Familiarity with domain and DNS concepts (such as typosquatting and homoglyphs) or with identity and impersonation signals. - Experience with large-scale data tooling (e.g., Databricks, Spark, Airflow) and distributed pipelines. - Experience with containerization and orchestration (Docker, Kubernetes) and infrastructure-as-code tooling. - Familiarity with modern frontend frameworks (e.g., React) for full-stack roles, or with ML/ML Ops for Detection/MLE-focused roles. - Prior experience in a fast-paced, high-growth startup environment where you've had to balance speed, quality, and ambiguity. ## Skills ### Required - Python - data structures - algorithms - system design - testing - debugging - SQL - AI-native development - LLM-based detection agents ### Nice to have - distributed systems - high-throughput pipelines - large-scale data stores - security - threat detection - anti-abuse - fraud detection - trust and safety - ML or LLM evaluation - domain and DNS concepts - identity and impersonation signals - large-scale data tooling - containerization and orchestration - infrastructure-as-code tooling - modern frontend frameworks - ML/ML Ops --- Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/abnormalsecurity/jobs/7774637003?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)