At Abnormal AI, we protect our customers against nefarious adversaries who are constantly evolving their techniques and tactics to outwit and undermine the traditional approaches to Security.
Abnormal is recognized as a top cybersecurity startup (Leader in the 2025 Gartner Magic Quadrant for Email Security Platforms), securing a Series D funding of $250 million at a $5.1 billion valuation in August 2024.
About The Team
This team owns the end-to-end development and operation of the infrastructure, ML models, AI agents, customer-facing APIs, and internal tools that power Abnormal's Identity Threat Protection (ITP) product. Our work is central to detecting malicious behavior and protecting customers from advanced identity-based attacks — including account takeover, identity spoofing, data leakage, and other high-impact threats.
About The Role
We are looking for a Software Engineer to help build and evolve our platform as it scales to meet expanding product requirements. This role blends hands-on backend systems development with growing ownership of features and production systems, including AI-powered and agentic components, working closely with senior engineers to improve system reliability, reduce latency, and accelerate feature release cycles.
What You'll Do
Technical Delivery & Excellence
- Design, build, and iterate on scalable backend and ML systems, APIs, frameworks, and internal tools.
- Take ownership of well-scoped features and components, with guidance from senior engineers on more complex, cross-system work.
- Contribute to the stability, reliability, and operational excellence of critical systems.
- Write clean, testable, and resilient code with attention to edge cases and performance.
- Contribute to technical design documents and participate in design discussions.
- Participate in code and design reviews, and contribute to on-call rotations.
AI Engineering & Agents
- Help design and build LLM-powered features and agentic workflows (e.g., automated investigation, triage, or remediation assistants) as part of the ATO platform.
- Integrate LLM APIs and agent frameworks into backend services, with attention to reliability, cost, latency, and evaluation.
- Contribute to prompt design, tool/function-calling integrations, and guardrails for AI-driven components under senior engineer guidance.
- Use GenAI coding assistants and agents as part of your own development workflow to accelerate delivery and testing.
Collaboration & Growth
- Collaborate with product managers, designers, and engineers to align on specifications and priorities.
- Break down well-defined projects into clear executable steps and drive them to completion.
- Contribute to roadmap discussions and share ideas for technical improvements.
- Communicate effectively in an async-first environment, providing clarity on updates, challenges, and solutions.
- Actively seek feedback and mentorship from senior engineers to accelerate your growth.
What We're Looking For
- Ownership & Growth: A proactive engineer who takes ownership of assigned work and is eager to grow into increasingly complex projects.
- Attention to Detail: Strong focus on code quality, reliability, monitoring, and performance.
- Solid Fundamentals: Good grounding in system design principles, with a growing ability to reason about scaling and reliability tradeoffs.
- Strong Collaborator: Comfortable working cross-functionally and in a distributed environment.
- AI & Agent Engineering Curiosity: Genuine interest in LLM-powered features and agentic systems, and proactive in leveraging modern developer productivity tools, including GenAI assistants and coding agents, to accelerate delivery.
Must-Have Skills
- 3-5 years of industry experience as a Software Engineer, with a track record of shipping production backend systems.
- Solid backend proficiency in Python, with experience building and maintaining production systems.
- Experience with system design fundamentals and building reliable, scalable applications.
- Working knowledge of relational databases and modern data storage technologies.
- Exposure to service-to-service communication (gRPC, Kafka) and caching (Redis) is a plus.
- Experience with AWS cloud services (S3, RDS) and deployment practices.
- Familiarity with containerization and orchestration (Docker, Kubernetes, Helm) is a plus.
- Understanding of service health, monitoring, and incident response practices.
- Comfortable writing technical documentation and contributing to design discussions.
Nice-to-Have Skills
- Hands-on experience integrating LLM APIs (e.g., OpenAI, Anthropic) into production applications.
- Exposure to agent frameworks or patterns (e.g., LangChain, LangGraph, tool/function calling, ReAct-style agents).
- Familiarity with prompt engineering, evaluation, and observability for AI-driven features.
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