Platform Engineer

About UsHarden is the automated DevSecOps platform for AI-generated code. We provide the security layer that turns AI-generated prototypes into compliant, hardened applications — automatically patching critical bugs, adding SSO and SCIM, and securely deploying to your cloud with zero DevOps overhead.We're an early-stage venture-backed startup based in the SF Bay Area. The founding team combines AI/ML research leadership at Google DeepMind and Amazon with product leadership at Verkada and AWS. We already have enterprise customers locked in and are bringing on exceptional engineers to build the infrastructure that makes enterprise AI adoption safe and scalable.About the RoleHarden is an opinionated DevSecOps layer that secures AI-generated applications by automatically injecting identity controls, network boundaries, and compliance instrumentation prior to deployment. Our focus is on the application and infrastructure layer surrounding AI systems, working alongside foundation model providers who advance model-level safety. We value engineers who can think adversarially about real-world deployments and translate that into resilient, production-grade systems.The ideal candidate blends enterprise security engineering, backend platform development, and solution architecture. This role requires deep experience with core SaaS security primitives such as SSO, SCIM, and RBAC, along with network isolation and SOC 2, ISO 27001, etc aligned controls. You will embed these capabilities directly into our automated pipeline and articulate their value in clear, executive-level terms for CISOs.What You'll DoDesign and implement reusable security primitives in Harden (SSO/OIDC integrations, SAML bridges, SCIM provisioning flows, RBAC policy engines, secure session management) that can be automatically injected into customer appsDefine and enforce deny-by-default network patterns, surrogate credential schemes, egress allowlists, and secrets isolation for agent code running in customer VPCsCollaborate with backend and platform engineers to build runtime security and observability features (policy evaluation, anomaly detection hooks, SIEM integrations, compliance dashboards) directly into the deployment pipelineContinuously research AI security threats and best practices—across MLSecOps, agent security, and AI DevSecOps—and translate them into concrete hardening rules and product features