Software Engineer - Backend - Behavioral Security Products
Abnormalsecurity
What this role requires
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Also mentioned, not required: ML, APIs, GenAI. Worth having, but their absence is not what gets a CV filtered out.
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Job description
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.
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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.
#LI-ML1
A note on AI in our process: Abnormal AI uses AI-assisted tools to help our recruiting team prepare for candidate interviews. These tools analyze resume content and role requirements to suggest interview questions and areas for the interviewer to explore.They do not make hiring decisions or screen candidates automatically. Every decision about a candidacy is made by a person. Further, if your application is successful and Abnormal AI makes a conditional offer of employment, we will carry out pre-employment checks which must be successfully completed to progress to a final offer. All processes and pre-employment checks are in line with prevailing legislation and Abnormal AI's policies relevant to our security and privacy standards.
Abnormal AI is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status or other characteristics protected by law. For our EEO policy statement please click here . If you would like more information on your EEO rights under the law, please click here .
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