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Salary: USD 238,000 - 326,000 / annual
Okta is seeking a Principal Machine Learning Engineer to join the Okta Secures AI team, specifically the Agent Access Policies sub-team. This is a career-defining opportunity to architect and advance Okta's authorization capabilities by replacing static, rule-based systems with dynamic AI security mechanisms.
You will design and implement intent-based enforcement systems that verify agent runtime requests match their intended purpose. Your work will involve applying LLM reasoning and prompt parsing to interpret prompts, tool payloads, and intent in real time. You'll integrate 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.
Key responsibilities include:
- Designing confidence-scored decision engines that feed semantic verification results into policy frameworks (e.g., Cedar)
- 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 mission is to transform identity from a reactive gatekeeper to a unified control plane for the agentic era, enabling enterprises to scale AI safely while ensuring every access decision—whether made by a human or automated agent—is authenticated, authorized, and audited at scale.
REQUIREMENTS:
- 10+ years of software development experience with strong programming expertise in Python (Go or TypeScript a plus)
- Hands-on experience with applied machine learning, from feature engineering to training and fine-tuning models
- Hands-on experience with modern Generative AI platforms (AWS Bedrock, OpenAI, Anthropic, etc.)
- Deep understanding of retrieval-augmented generation (RAG), embeddings, and knowledge-base workflows
- 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.)
- 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, navigate unknowns, and iterate quickly
- Experience building tools or infrastructure for AI/ML applications with deep understanding of the developer lifecycle in an AI-native world
- Bachelor's or Master's degree in Computer Science or related field
EXTRA CREDIT:
- 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
