Staff Machine Learning Engineer, Okta

• Strong programming expertise in Python (and familiarity with Go or Typescript a plus)
• 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
• Familiarity with ML frameworks (FastAPI, PyTorch, TensorFlow, Spark ML) and workflow orchestration tools (Airflow, etc.)
• Experience with LiteLLM, LangGraph, LangChain, DSPy, LlamaIndex, MCP, or other related AI agent frameworks
• 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 and frequently
• Experience building tools or infrastructure for AI/ML applications, with a deep understanding of the developer lifecycle in an AI-native world
• Please note that we encourage candidates to apply even if you do not have experience with all of the criteria or technologies listed above; these are provided to give insight into the tech stack and general responsibilities for the role

Desirable

• As a Staff Machine Learning Engineer on the Generative AI team, you will help shape and accelerate our Generative AI strategy by contributing across the stack of model development, infrastructure, and platform services. You’ll drive design and implementation of production-ready AI/ML systems at scale—ranging from LLM-powered features to reusable platform components that other teams across Okta can build on
• Design and implement scalable infrastructure and platform services for large-scale Generative AI use cases
• Drive technical decision making while striving to hit the right balance between factors such as simplicity, flexibility, reliability, and performance
• Architect, design, and deploy robust Machine Learning & GenAI systems, ensuring seamless integration with diverse platform services and establishing scalable LLMOps pipelines in production
• Lead initiatives to tune, optimize, and deploy agentic applications in production with a focus on performance, reliability, and security
• Partner with Product, Security, and Platform Engineering teams to design AI-powered experiences that are both innovative and trustworthy
• Collaborate cross-functionally with product managers, researchers, and engineers to deliver secure, high-quality, and scalable AI/ML systems
• Design and implement components across different layers of the stack using technologies such as Python, LangGraph, DSPy, and MCP
• Contribute to the design of scalable, observable ML and Generative AI systems that integrate retrieval, inference, and evaluation pipelines
• Develop and iterate on structured prompting, context retrieval, and RAG workflows that improve accuracy, safety, and cost efficiency in Claude-based systems
• Build and refine automated evaluation pipelines to measure model quality, correctness, groundedness, and safety in production
• Implement schema validation, structured output enforcement, and other guardrails that keep AI outputs reliable, auditable, and compliant with enterprise standards
• Mentor and coach engineers, contributing to the growth of the team and the larger engineering community