Principal Machine Learning Engineer (SecureAI)

• Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organizations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence
• This is an opportunity to do career-defining work. We’re all in on this mission. If you are too, let’s talk
• In this role, you will join the Agent Access Policies sub-team under Okta Secures AI as a Principal Machine Learning (ML) Engineer to advance Okta’s authorization capabilities. By replacing static, rule-based systems with dynamic AI security mechanisms, you will deliver real-time threat inspection, agent intent evaluation, and behavioral analysis—ensuring autonomous AI agents operate safely within specified bounds
• Your core mission will be architecting and driving features for our unified control plane to establish the premier Identity Security Fabric for the agentic era. In doing so, you will help execute our guiding principle: “To get AI right, you have to get identity right.”
• Implement intent-based enforcement to verify agent runtime requests match their intended purpose, requiring key capabilities
• Apply LLM reasoning and prompt parsing to interpret prompts, tool payloads, and intent in real time
• Integrate low-latency inference or semantic evaluation engines 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 that feed 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 and Generative AI systems integrating retrieval, inference, and evaluation pipelines
• Optimize prompting, context retrieval, and RAG workflows for accuracy, safety, and efficiency in 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 to ensure reliable, compliant AI outputs
• Mentor and coach engineers to support team and community growth

Benefits

• Work from home opportunities
• Health + Wellness
• Financial Benefits
• Pay + Incentives
• Time Off
• Everyday Living
• Resources- Deep understanding of retrieval-augmented generation (RAG), embeddings, and knowledge-base workflows
• Experience building tools or infrastructure for AI/ML applications, with a deep understanding of the developer lifecycle in an AI-native world
• Hands-on experience with LiteLLM, LangGraph, LangChain, LlamaIndex, MCP, or other related AI agent frameworks
• Familiarity with ML frameworks (FastAPI, PyTorch, TensorFlow, Spark ML) and workflow orchestration tools (Airflow, etc.)
• 10+ years of software development experience, with strong programming expertise in Python (and familiarity with Go or Typescript a plus)
• Hands-on experience with applied machine learning, from feature engineering to training and fine-tuning models
• Experience defining evaluation metrics, pipelines, and feedback loops for ML/GenAI systems
• Hands-on experience with modern Generative AI platforms (AWS Bedrock, OpenAI, Anthropic, etc.)
• Proven ability to collaborate with product and engineering teams to drive greenfield initiatives forward, navigate unknowns, and iterate quickly and frequently
• Experience integrating AI-driven systems with identity, authentication, or security products
• Exposure to ethical AI, model risk, or compliance frameworks
• Familiarity with evaluation datasets, synthetic data generation, or LLM-as-a-judge methods