AI Security Engineer
Role details
Job location
Tech stack
Job description
AI is the fastest-moving attack surface in the enterprise. Copilot, ChatGPT Enterprise, Gemini, Claude, and a wave of agentic tools are being wired into corporate SaaS faster than security teams can keep up - with broad OAuth scopes, opaque data flows, and non-human identities acting on behalf of employees. Obsidian sits directly in the path of this shift.
As an AI Security Engineer, you'll define how Obsidian understands, identifies, and mitigates risk in AI and agentic platforms. You'll own the threat models and data modeling that protect customers - and you'll work in the data yourself, because in this space the security judgment and the query are inseparable.
What You'll Do
- Develop deep expertise in how AI platforms (ChatGPT Enterprise, Copilot, Gemini, Claude, Glean, agentic frameworks) authenticate, what scopes they request, and where risk concentrates.
- Research emerging AI attack techniques - prompt injection, tool/agent misuse, RAG poisoning, over-permissioned integrations - and turn them into detections and posture checks.
- Build threat models covering identity, data access, non-human identities, and integration risk.
- Prototype and ship product features end-to-end against Obsidian's AI and SaaS telemetry.
- Build and improve data pipelines for high efficiency, development velocity, and low cost.
Requirements
- 3+ years in security engineering as a builder, with clear ownership of shipped work.
- Strong grasp of identity and access control concepts
- Hands-on experience with a modern data stack (DBT and a columnar warehouse or query engine such as ClickHouse, Snowflake, BigQuery, or Databricks).
- Clear communicator across engineers, PMs, and customer security teams.
Nice to Have
- Strong grasp of how SaaS platforms (Google Workspace, Microsoft 365, Salesforce, Okta) expose data - APIs, event schemas, permissions, auth flows.
- Deep knowledge of AI and SaaS security - OWASP LLM Top 10, MITRE ATLAS, prompt injection, agent/tool-use risks, OAuth abuse, and identity models across major AI and SaaS platforms.