The Team:

Have you ever considered what powers the intelligent features behind seamless product experiences? The GenAI team is at the forefront of enabling AI-powered security and intelligent innovation across our organization. From crafting AI powered security services, to intuitive generative AI-powered chat experiences that provide instant product support, and developing the best developer experience around authentication for generative AI and AI agents, our team is instrumental in bringing the transformative power of AI to life. We collaborate closely with the Machine Learning team and various product teams to ensure the seamless and secure delivery of AI-enhanced features that provide real value to our users.

The Opportunity: 

As a Staff Machine Learning Engineer on the Generative AI team, you will help shape, architect, 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 components that other teams across Okta can build on.

You will have the opportunity to:

• Architect, design, and deploy robust Machine Learning & GenAI systems, ensuring seamless integration with diverse platform services and establishing scalable LLMOps pipelines in production.
• Drive technical decision making while striving to hit the right balance between factors such as simplicity, flexibility, reliability, and performance.
• 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.
• Design and implement scalable infrastructure and platform services for large-scale Generative AI use cases.
• Collaborate cross-functionally with product managers, researchers, and engineers to deliver secure, high-quality, and scalable AI/ML systems.

 What you will do:

• Spearhead 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.

What you bring:

• 7+ 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.
• 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 and frequently.
• Experience building tools or infrastructure for AI/ML applications, with a deep understanding of the developer lifecycle in an AI-native world.

Nice to haves:

• 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.

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.

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