Software Engineer II Entity Intelligence

About the Role

Enterprises of all sizes trust Abnormals AI-native security products to stop cybercrime and protect critical communications identities and infrastructure in the cloud. Our products are data- and systems-intensive operating at high scale and low latency across multiple clouds and regions.

As a Software Engineer II on the Entity Intelligence 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. You will work with a world-class group of engineers product managers and data scientists to build and operate detection that is reliable scalable and AI-native by default.

This role focuses on impersonation detection including brand lookalike-domain VIP and employee impersonation. It is ideal for an engineer who has already shipped meaningful production systems wants more ownership and impact and is excited to use AI to build detection that was not possible before.

About the Team

The Entity Intelligence Team (EIT) is an attack-detection team inside Abnormals Detection org. We own several of the highest-visibility detection surfaces at the company spanning attachment-based attacks fraud and impersonation. We work the way an analyst would: we study the attacks that get through understand the underlying pattern and translate it into system-level detection enhancements that generalize beyond the individual attack.

We are also one of the most AI-forward teams at Abnormal. We build and operate LLM-based detection agents and treat internal AI tooling as a first-class deliverable. Every engineer here writes detection logic and builds AI agents. Impersonation is one of the most damaging and visible classes of attack we defend against where even simple attacks that slip through erode customer trust so this is a surface we hold to a very high bar.

What Youll Do

• Design build and operate detection that is core to Abnormals 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.

Must Haves

• 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. We make decisions in writing.
• A strong growth mindset and sense of ownership.

Nice to Have Skills

• 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 youve had to balance speed quality and ambiguity.

Why Youll Love It Here

• Youll solve hard meaningful problems at the intersection of AI security and large-scale detection where your work maps directly to attacks caught and customers protected.
• Youll work with smart kind and ambitious teammates who care deeply about detection craft learning and helping each other grow.
• Youll get real ownership and autonomy over an important detection surface not a ticket queue with clear opportunities to grow toward Senior and Staff roles.
• Youll be part of an AI-native R&D organization with strong investment in tools workflows and training to help engineers use AI to move faster while raising the quality bar.

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