# Principal Machine Learning Engineer, SecureAI **Company**: Okta **Location**: San Francisco, California **Work arrangement**: hybrid **Experience**: senior **Job type**: full-time **Salary**: $238,000-$326,000 USD **Category**: Engineering **Industry**: Technology **Apply**: https://job-boards.greenhouse.io/okta/jobs/8208377?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_61c3fa3b-e76 ## Description We are seeking a Principal Machine Learning Engineer to join the Okta Secures AI Team. The successful candidate will help build the infrastructure that allows enterprises to scale AI safely. The Okta for AI Agents Team is building the future of digital identity management. The team's mission is to get AI right by getting identity right. The role involves: - Implementing intent-based enforcement to verify agent runtime requests match their intended purpose - Applying LLM reasoning and prompt parsing to interpret prompts, tool payloads, and intent in real time - Integrating low-latency inference or semantic evaluation engines directly into the API gateway request path - Using embeddings, vector search, or zero-shot classification to score alignment between agent intent and executed actions - Designing confidence-scored decision engines that feed semantic verification results into policy frameworks - Establishing evaluation benchmarks, prompt injection defenses, and guardrails to prevent bypasses or false positives - Architecting scalable ML and Generative AI systems integrating retrieval, inference, and evaluation pipelines - Optimizing prompting, context retrieval, and RAG workflows for accuracy, safety, and efficiency in Claude-based systems - Building automated evaluation pipelines to measure model quality, correctness, groundedness, and safety in production - Implementing schema validation, structured output enforcement, and guardrails to ensure reliable, compliant AI outputs - Mentoring and coaching engineers to support team and community growth The ideal candidate will have: - 10+ years of software development experience, with strong programming expertise in Python - Hands-on experience with applied machine learning, from feature engineering to training and fine-tuning models - Hands-on experience with modern Generative AI platforms - Deep understanding of retrieval-augmented generation, embeddings, and knowledge-base workflows - Hands-on experience with LiteLLM, LangGraph, LangChain, LlamaIndex, MCP, or other related AI agent frameworks - Familiarity with ML frameworks and workflow orchestration tools - 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 forward - Experience building tools or infrastructure for AI/ML applications Extra credit will be given for experience integrating AI-driven systems with identity, authentication, or security products, exposure to ethical AI, model risk, or compliance frameworks, and familiarity with evaluation datasets, synthetic data generation, or LLM-as-a-judge methods. In terms of compensation, the annual base salary range for this position for candidates located in the San Francisco Bay area is between $238,000-$326,000 USD. Okta also offers equity, bonus, and benefits, including health, dental and vision insurance, 401(k), flexible spending account, and paid leave. ## Skills ### Required - Python - applied machine learning - Generative AI platforms - retrieval-augmented generation - embeddings - knowledge-base workflows - LiteLLM - LangGraph - LangChain - LlamaIndex - MCP - ML frameworks - workflow orchestration tools ### Nice to have - integrating AI-driven systems with identity, authentication, or security products - ethical AI - model risk - compliance frameworks - evaluation datasets - synthetic data generation - LLM-as-a-judge methods --- Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/okta/jobs/8208377?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)