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Okta is seeking a Staff Machine Learning Engineer to join the Generative AI team and help shape the company's AI strategy. You will architect and deploy production-ready AI/ML systems at scale, ranging from LLM-powered features to reusable platform components that enable other teams across Okta to build on.
Key responsibilities include:
- Architect, design, and deploy robust ML and GenAI systems with seamless integration into platform services and scalable LLMOps pipelines
- Drive technical decision-making, balancing simplicity, flexibility, reliability, and performance
- Lead initiatives to tune, optimize, and deploy agentic applications in production with focus on performance, reliability, and security
- Partner with Product, Security, and Platform Engineering teams to design trustworthy AI-powered experiences
- Design and implement scalable infrastructure and platform services for large-scale GenAI use cases
- Spearhead design of observable ML systems integrating retrieval, inference, and evaluation pipelines
- Develop structured prompting, context retrieval, and RAG workflows using Claude-based systems
- Build automated evaluation pipelines measuring model quality, correctness, groundedness, and safety
- Implement schema validation, structured output enforcement, and guardrails for reliable, auditable, compliant AI outputs
- Mentor and coach engineers, contributing to team and community growth
Required qualifications:
- 7+ years software development experience with strong Python expertise (Go or TypeScript a plus)
- Hands-on applied ML experience from feature engineering through model training and fine-tuning
- Experience with modern GenAI platforms (AWS Bedrock, OpenAI, Anthropic, etc.)
- Deep understanding of RAG, embeddings, and knowledge-base workflows
- Hands-on experience with AI agent frameworks (LiteLLM, LangGraph, LangChain, LlamaIndex, MCP)
- Familiarity with ML frameworks (FastAPI, PyTorch, TensorFlow, Spark ML) and orchestration tools (Airflow)
- Experience defining evaluation metrics, pipelines, and feedback loops for ML/GenAI systems
- Proven ability to collaborate across teams on greenfield initiatives, navigate unknowns, and iterate rapidly
- Experience building tools or infrastructure for AI/ML applications
Nice-to-haves include experience integrating AI systems with identity/authentication/security products, exposure to ethical AI and compliance frameworks, and familiarity with evaluation datasets and synthetic data generation.
