AI Security Engineer

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services.

This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.

Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.

About the Role

Cerebras is seeking a Principal AI Security Engineer to lead hands-on security engineering for enterprise IT, infrastructure, AI platforms, and agentic systems.

In this role, you will design and build security controls for systems that support training, inference, model serving, customer workloads, internal automation, and AI-assisted development. You will work across product, cloud, infrastructure, identity, runtime, data, and developer platforms to protect sensitive data, enterprise and customer environments, models, tools, agents, and control planes.

This is a principal IC role for someone who can turn ambiguous AI and platform security risks into practical architecture, reusable controls, and production-ready systems that teams can adopt by default.

Responsibilities

• 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.