Security Engineer, Detection and Response (uk)

Writer 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 detection systems, automated response playbooks, leading incident response, proactive threat hunting across GPU clusters and training environments, and developing detection-as-code frameworks. It requires collaboration with AI Security research, Cloud Infrastructure, and Software Security Engineering teams to protect enterprise-grade AI platforms.

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
  • 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
  • Demonstrated ability to build detection for novel attack techniques
  • Ability to conduct forensics in complex distributed environments
  • Self-directed execution mindset
  • Track record of securing high-value intellectual property
  • Track record of automating incident response in complex environments
  • Ability to identify critical security gaps through proactive threat hunting

Nice to have

  • Security guardrails

What the JD emphasized

  • AI-specific threats
  • automated response
  • GPU clusters
  • distributed training environments
  • AI infrastructure
  • model deployments
  • training data
  • model weights
  • inference endpoints
  • training pipeline attacks
  • model manipulation attempts
  • model outputs
  • AI-specific vulnerabilities
  • visibility gaps
  • detection-as-code frameworks
  • AI training infrastructure
  • inference endpoints
  • model security metrics
  • GPU utilization patterns
  • sensitive research data
  • operational security partner
  • AI Security's threat research
  • Cloud Infrastructure's GPU clusters
  • customer-impacting incidents
  • responsible AI development
  • security guardrails
  • critical AI security incidents
  • real-time threats
  • detection coverage
  • automation capabilities
  • AI systems evolve
  • securing AI/ML infrastructure
  • high-performance computing environments
  • distributed systems at scale
  • Python
  • KQL
  • SPL
  • custom detection logic
  • automate response workflows
  • operationalize security at scale
  • cloud-native and distributed computing environments
  • SIEM platforms
  • detection technologies
  • forensic investigation techniques
  • novel attack techniques
  • forensics in complex distributed environments
  • Self-directed execution mindset
  • securing high-value intellectual property
  • automating incident response
  • complex environments
  • identifying critical security gaps
  • proactive threat hunting
  • Connect across security, infrastructure, and AI research teams
  • build comprehensive defenses
  • Challenge assumptions

Other signals

  • AI-specific threats
  • automated response capabilities
  • defending cutting-edge AI/AGI systems
  • GPU clusters and distributed training environments
  • operational security partner