Staff Machine Learning Engineer, Generative AI (Auth0)

Okta•Toronto, ON

3d•CA$168,000 - CA$231,000•Hybrid

About The Position

Okta is seeking a Staff Machine Learning Engineer specializing in Generative AI to join their GenAI team. This role is crucial for shaping and accelerating Okta's Generative AI strategy, contributing to model development, infrastructure, and platform services. The engineer will design and implement production-ready AI/ML systems at scale, including LLM-powered features and reusable components for other teams. The GenAI team focuses on enabling AI-powered security and intelligent innovation across the organization, from AI security services to generative AI-powered chat experiences and developer tools for AI agents.

Requirements

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.

Responsibilities

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.
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.