Security Engineer, Detection and Response (uk)

Writer · AI Frontier · London, United Kingdom · Engineering, product & design

Security engineer focused on detecting and responding to threats targeting AI infrastructure, training data, and model deployments. This role involves building sophisticated detection systems, automated response capabilities, and proactive threat hunting across GPU clusters and distributed training environments, with a focus on AI-specific attack vectors.

What you'd actually do

  1. Design and implement detection strategies that identify AI-specific threats including prompt injection, model extraction, data poisoning, adversarial examples, and unauthorized access to training datasets or model weights across our distributed infrastructure
  2. Build automated response playbooks and orchestration workflows that contain threats without human intervention, creating self-healing security systems that reduce mean time to response from hours to minutes while automatically remediating compromised inference endpoints
  3. Lead security incident response coordination across all teams (Cloud, AppSec, Enterprise, AI Security) when AI infrastructure or models are compromised, conducting forensic investigations on training pipeline attacks and model manipulation attempts while drafting clear incident communications for engineering and executive leadership
  4. Hunt proactively for sophisticated threats across GPU clusters and training infrastructure by analyzing model outputs for signs of compromise, reproducing AI-specific vulnerabilities from security research, and identifying visibility gaps in distributed training environments before adversaries exploit them
  5. Build detection-as-code frameworks with version control and automated deployment, onboard telemetry from AI training infrastructure and inference endpoints, and create dashboards that track model security metrics, GPU utilization patterns, and access to sensitive research data

Skills

Required

  • 3-5+ years in security operations, detection engineering, or incident response with a proven track record of identifying and stopping sophisticated attacks in production environments
  • 3+ years specifically securing AI/ML infrastructure, high-performance computing environments, or other distributed systems at scale
  • Strong programming skills in Python, KQL, SPL, or similar languages
  • Experience with SIEM platforms, detection technologies, and forensic investigation techniques
  • Self-directed execution mindset
  • Deep alignment with WRITER's values

Nice to have

  • building custom detection logic
  • automate response workflows
  • create tools that operationalize security at scale across cloud-native and distributed computing environments
  • demonstrated ability to build detection for novel attack techniques that don't have established patterns yet
  • conduct forensics in complex distributed environments
  • track record of securing high-value intellectual property
  • automating incident response in complex environments
  • identifying critical security gaps through proactive threat hunting before they become incidents

What the JD emphasized

  • AI-specific threats
  • AI infrastructure
  • model deployments
  • GPU clusters
  • distributed training environments
  • prompt injection
  • model extraction
  • data poisoning
  • adversarial examples
  • unauthorized access to training datasets or model weights
  • inference endpoints
  • training pipeline attacks
  • model manipulation attempts
  • AI Security research team
  • Cloud Infrastructure
  • Software Security Engineering
  • AI researchers
  • securing systems that are fundamentally different
  • AI security engineering at scale
  • novel threats that don't exist in textbooks yet
  • securing AI/ML infrastructure
  • high-performance computing environments
  • distributed systems at scale
  • novel attack techniques that don't have established patterns yet
  • forensics in complex distributed environments
  • securing high-value intellectual property
  • automating incident response in complex environments
  • identifying critical security gaps through proactive threat hunting

Other signals

  • AI-specific threats
  • AI infrastructure
  • model deployments
  • GPU clusters
  • distributed training environments