Principal AI Security Engineer

Cerebras Systems

Principal AI Security Engineer at Cerebras Systems designing security controls for AI platforms and customer workloads. Collaborating across product and infrastructure teams to enhance security systems.

Posted 6/23/2026full-timeRemote • California • 🇺🇸 United StatesLeadWebsite

Tech Stack

Tools & technologies

AWSCloudKubernetesPython

About the role

Key responsibilities & impact

Define security architecture and build controls for AI platforms, training and inference workflows, model-serving systems, customer workloads, developer workflows, and agentic
Develop reusable AI and agent security patterns for identity, authorization, delegated authority, scoped tool access, MCPs, connectors, secrets, approvals, isolation, auditability, and
Design runtime controls that constrain execution, access, data exposure, model and tool interaction, and blast radius.
Build security capabilities as code using infrastructure as code, configuration as code, policy as code, GitOps, CI/CD, and automated validation.
Define secure development patterns for AI systems, agents, prompts, tools, models, policies, evaluations, releases, and rollback.
Automate security reviews, policy checks, evidence collection, control validation, and remediation
Instrument AI, agent, and platform activity with telemetry, traceability, policy decisions, audit logs, anomaly signals, and response workflows.
Lead hands-on security reviews and influence product, platform, infrastructure, and security architecture through practical design changes and reusable controls.

Requirements

What you’ll need

10+ years of experience in security engineering, platform security, infrastructure security, product security, or related technical security roles.
Strong hands-on engineering ability in Python and at least one additional production
Experience designing, building, operating, and improving security controls as
Strong cloud and infrastructure security experience, preferably with AWS, including IAM, networking, secrets management, logging, and cloud-native control planes.
Deep understanding of identity and access systems, including SSO, MFA, OAuth, service accounts, workload identity, authorization, privileged access, and least privilege.
Practical experience securing runtime environments such as containers, Kubernetes, isolated workloads, secure development environments, distributed compute platforms, or production service infrastructure.
Familiarity with AI security, LLM application security, agentic workflows, MCPs, prompt injection, autonomous coding agents, or AI platform security.
Ability to reason about cross-system risk involving identity, data, models, tools, networks, workflows, approvals, and automation.
Strong written communication skills and the ability to influence senior technical stakeholders across Security, Product, IT, Infrastructure, and Engineering.

Benefits

Comp & perks

Health insurance
401(k) matching
Flexible work hours
Paid time off
Professional development opportunities

ATS Keywords

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Hard Skills & Tools

Pythoncloud securityinfrastructure as codeconfiguration as codepolicy as codeGitOpsCI/CDsecurity controlsruntime environmentsAI security

Soft Skills

strong written communicationinfluence senior technical stakeholdersreasoning about cross-system risk