Software Engineer – Backend – Behavioral Security Products

Job not on LinkedIn

🔥 0 minutes ago

🇬🇧 United Kingdom – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🔙 Backend Engineer

👻 Ghost score 12%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Abnormal Security

WebsiteLinkedInAll Job Openings

501 - 1000 employees

🔒 Cybersecurity

Cybersecurity

Abnormal Security is a provider of cloud-native email security solutions that specializes in preventing a wide array of cyber threats. The company focuses on protecting businesses from attacks such as phishing, malware, ransomware, and social engineering, ensuring total protection for corporate email systems. Their offerings include business email security, phishing detection, and securing against account takeovers, making them a leader in the computer and network security space.

📋 Description

• Design, build, and iterate on scalable backend and ML systems, APIs, frameworks, and internal tools • Own well-scoped features and components, with guidance from senior engineers on 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 • Help design and build LLM-powered features and agentic workflows for automated investigation, triage, or remediation assistants • Integrate LLM APIs and agent frameworks into backend services, considering reliability, cost, latency, and evaluation • Contribute to prompt design, tool/function-calling integrations, and guardrails for AI-driven components • Use GenAI coding assistants and agents in the development workflow to accelerate delivery and testing • Collaborate with product managers, designers, and engineers to align on specifications and priorities • Break down well-defined projects into executable steps and drive them to completion • Contribute to roadmap discussions and share ideas for technical improvements • Communicate updates, challenges, and solutions in an async-first environment • Seek feedback and mentorship from senior engineers

🎯 Requirements

• 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 • Experience with AWS cloud services, including S3 and RDS, and deployment practices • Understanding of service health, monitoring, and incident response practices • Comfortable writing technical documentation and contributing to design discussions • Proactive ownership of assigned work and eagerness to grow into increasingly complex projects • Strong focus on code quality, reliability, monitoring, and performance • Ability to work cross-functionally and in a distributed environment • Genuine interest in LLM-powered features and agentic systems, and willingness to leverage GenAI assistants and coding agents • Exposure to service-to-service communication, such as gRPC and Kafka, and caching with Redis is a plus • Familiarity with containerization and orchestration, such as Docker, Kubernetes, and Helm, is a plus • Hands-on experience integrating LLM APIs, such as OpenAI and Anthropic, into production applications is nice to have • Exposure to agent frameworks or patterns, such as LangChain, LangGraph, tool/function calling, and ReAct-style agents, is nice to have • Familiarity with prompt engineering, evaluation, and observability for AI-driven features is nice to have

🏖️ Benefits

• AI-assisted recruiting tools are used to prepare for candidate interviews, but do not make hiring decisions or screen candidates automatically • Pre-employment checks in line with prevailing legislation and Abnormal AI's security and privacy standards • Equal opportunity employer