Staff Machine Learning Engineer, Generative AI (Auth0)

• 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.

• 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.

• 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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