Principal Machine Learning Engineer, SecureAI
Okta · San Francisco, California
About this role
**Secure Every Identity, from AI to Human** Identity is the key to unlocking the potential of AI. Okta Secures AI builds trusted, neutral infrastructure that enables organizations to safely embrace this new era—solving complex challenges with real-world stakes. **About the Okta Secures AI Team** The Okta for AI Agents Team is building the future of digital identity management. As companies deploy AI agents with access to critical tools, teams need to move fast while staying secure. Our mission is to evolve identity from a reactive gatekeeper to a unified control plane—transforming AI risk into business ROI. We’re building the industry’s first **Identity Security Fabric for the agentic era**, using a four-pillar maturity model: - **Discover** - **Onboard** - **Protect** - **Govern** **About the role (Principal Machine Learning Engineer)** Join the **Agent Access Policies** sub-team to advance Okta’s authorization capabilities. You’ll replace static, rule-based systems with dynamic AI security mechanisms to deliver **real-time threat inspection, agent intent evaluation, and behavioral analysis**—so autonomous AI agents operate safely within specified bounds. **What you’ll be doing** - Implement **intent-based enforcement** to verify agent runtime requests match their intended purpose. - Use **LLM reasoning and prompt parsing** to interpret prompts, tool payloads, and intent in real time. - Integrate **low-latency inference / semantic evaluation** directly into the API gateway request path. - Use **embeddings, vector search, or zero-shot classification** to score alignment between agent intent and executed actions. - Design **confidence-scored decision engines** feeding semantic verification results into policy frameworks (e.g., **Cedar**). - Establish **evaluation benchmarks**, **prompt injection defenses**, and **guardrails** to prevent bypasses or false positives. - Architect scalable **ML + Generative AI** systems integrating retrieval, inference, and evaluation pipelines. - Optimize **prompting, context retrieval, and RAG workflows** for accuracy, safety, and efficiency (e.g., Claude-based systems). - Build automated evaluation pipelines to measure **model quality, correctness, groundedness, and safety** in production. - Implement **schema validation**, **structured output enforcement**, and guardrails for reliable, compliant AI outputs. - Mentor and coach engineers to support team and community growth. **What you’ll bring to the role** - **10+ years** of software development experience; strong **Python** skills (Go or TypeScript a plus). - Hands-on applied ML experience (feature engineering through training/fine-tuning). - Hands-on experience with modern **Generative AI platforms** (AWS Bedrock, OpenAI, Anthropic, etc.). - Deep understanding of **RAG**, embeddings, and knowledge-base workflows. - Hands-on experience with agent frameworks such as **LiteLLM, LangGraph, LangChain, LlamaIndex, MCP**, or similar. - Familiarity with ML frameworks (FastAPI, PyTorch, TensorFlow, Spark ML) and orchestration tools (e.g., Airflow). - Experience defining **evaluation metrics**, pipelines, and feedback loops for ML/GenAI systems. - Proven ability to collaborate with product and engineering teams to drive greenfield initiatives, navigate unknowns, and iterate quickly. **Extra credit** - Experience integrating AI-driven systems with **identity/authentication/security** products. - Exposure to **ethical AI, model risk, or compliance** framewo
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