Principal AI Security Engineer

Responsibilities

• Define security architecture and build controls for AI platforms, training and inference workflows, model-serving systems, customer workloads, developer workflows, and agentic systems.
• Develop reusable AI and agent security patterns for identity, authorization, delegated authority, scoped tool access, MCPs, connectors, secrets, approvals, isolation, auditability, and governance.
• 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

• 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 programming language.
• Experience designing, building, operating, and improving security controls as production systems.
• 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 ability to influence senior technical stakeholders across Security, Product, IT, Infrastructure, and Engineering.

Relevant Experience

• AI, ML, training, inference, model serving, or large-scale compute.
• Coding agents, agent platforms, MCP servers, internal developer platforms, or AI-assisted development environments.
• Workload identity, secrets brokers, token brokers, short-lived credentials, privileged access, or zero-standing-privilege architectures.
• Policy as code, authorization services, runtime enforcement layers, or security control platforms.
• Software delivery security, including source control, CI/CD, build systems, artifacts, provenance, signing, and release gates.
• Detection, investigation, and response workflows for cloud, infrastructure, identity, AI, or agents.

Compensation and Benefits

• Build a breakthrough AI platform beyond the constraints of the GPU.
• Publish and open source cutting-edge AI research.
• Work on one of the fastest AI supercomputers in the world.
• Enjoy job stability with startup vitality.
• Work in a simple, non-corporate culture that respects individual beliefs.
• Continuous learning, growth, and support.