Senior Security Engineer, Abnormal AI

• This is a role for engineers who are intellectually curious and motivated to bridge security principles, engineering execution, and AI-accelerated delivery
• An intellectually curious, solution-focused engineer with a security mindset who thrives in fast-paced environments
• A technical leader who can architect scalable security solutions while maintaining engineering velocity — increasingly by directing AI to do the first draft and applying judgment to the result
• Someone who thinks like an attacker but builds like a defender, and who treats AI-generated infrastructure and detection logic with the same scrutiny as a junior engineer's first PR
• Someone with high agency who proactively spots what in their own workflow (and the team's) AI can absorb, and actually builds the automation to make that real — not someone waiting to be told
• A collaborative engineer who can translate security requirements into actionable engineering tasks
• A mentor who scales their judgment across the team — reviewing AI-generated code and infra for security gaps, and encoding what they learn into reusable playbooks and standards rather than repeating the same feedback one engineer at a time
• Proven delivery in security engineering or infrastructure security roles, ideally in cloud-native environments
• Deep comprehension of native AWS architecture services and identity/access patterns — IAM, STS, cross-account roles and resource policies, VPC and network segmentation, KMS — with AWS as our primary cloud platform, plus working knowledge of Azure and GCP
• Strong scripting and dev fundamentals in Python and/or Go — enough to read, critique, and confidently modify AI-generated code, not just prompt for it; proficiency with Git, Linux, and infrastructure automation patterns
• Demonstrated fluency directing AI coding/agent tools (e.g., Claude Code, Cursor, Copilot) to accelerate delivery, paired with the judgment to catch when AI-generated infrastructure or security logic is wrong, incomplete, or dangerous
• Expertise in integrating or building tooling for SIEM, SOAR, vulnerability management, and CSPM platforms
• Experience deploying security controls via Infrastructure-as-Code (Terraform or CloudFormation), primarily in AWS
• Comfortable investigating logs, tracing events, and contributing to incident analysis workflows
• Proven ability to influence and collaborate cross-functionally with engineering, infra, product, and IT
• Strong written communication and documentation skills and being able to convey complex designs clearly
• Background with using and securing container orchestration (Kubernetes), including workload security and service mesh controls

Desirable

• Abnormal AI is looking for a Senior Cyber Security Engineer to help build the next generation of security capabilities at scale
• This is a senior IC-level role that blends deep security expertise with the judgment to direct AI tools effectively — you'll increasingly rely on AI to scaffold Terraform modules, prototype detection logic, and draft integrations, while you own the architecture decisions, validate what ships, and catch the failure modes AI won't flag on its own (overprivileged roles, missed edge cases, subtle logic gaps)
• As a technical lead, you will own the architecture and correctness of systems that enhance both preventative guardrails and detective capabilities across our primarily AWS-based cloud environment, partnering closely with both platform engineering and product teams on security architecture reviews
• You'll raise the AI leverage of the engineers around you — not by teaching scripting basics, but by turning your review judgment into reusable standards the team can build on directly. You'll act as a technical liaison across teams and contribute directly to keeping our platforms and customers secure
• Lead threat modeling and security design discussions with both platform and product teams, translating risks into engineering actions and using AI to rapidly stress-test designs against attack scenarios before committing engineering time to a direction
• Partner with product engineering to review the security architecture of new product features — assessing designs for data exposure, access control, and abuse-case risk before they ship — using AI to speed up first-pass analysis so your review time goes toward the highest-risk decisions
• Collaborate with Platform, Infra, and DevOps teams to build scalable preventative controls in AWS via Infrastructure-as-Code — using AI coding agents to scaffold and iterate on Terraform/CloudFormation modules, reserving your own time for least-privilege review, blast-radius analysis, and edge cases
• Evaluate and uplift security tooling across commercial, cloud-native, and AI-assisted capabilities, focusing on scale, efficiency, and precision
• Mentor engineers on how to get real leverage from AI — reviewing AI-generated code and infrastructure together, and turning recurring feedback into standards and reusable playbooks instead of repeating it review after review
• Use AI to rapidly prototype automation for signal correlation, alert enrichment, and auto-remediation of known failure patterns, then harden and productionize what proves out rather than hand-building every workflow from a blank file
• Architect, build, and validate integrations between AWS and other cloud-native infrastructure and security tooling (e.g., SIEM, SOAR, IAM tooling), with a growing share of first-draft code AI-generated and human-owned
• Serve as a hands-on technical contributor during security incidents, using AI to accelerate log and telemetry triage while owning the judgment calls that determine root cause and response
• Build and maintain reusable AI-assisted playbooks (for IaC security review, detection authoring, alert triage) that scale your judgment across the security and platform teams