Engineering Manager - Customer Experience AI

Coinbase seeks an Engineering Manager to lead the CXAI team in building AI-driven customer support solutions within its Consumer Engagement organization.

This position is based within the Consumer Engagement and Experience (CEE) organization at Coinbase. The successful candidate will lead the development of an automated resolution platform that utilizes generative AI to transform how crypto support is delivered to users.

Responsibilities

• Manage the strategic roadmap for the conversational AI system, transitioning from traditional decision trees to advanced LLM reasoning, retrieval-augmented generation (RAG), and agentic workflows.
• Oversee the integration of external AI tools while expanding internal large language model infrastructure to handle high-volume, critical crypto support scenarios.
• Create evaluation systems and feedback mechanisms to enhance model precision, data grounding, regulatory compliance, and overall customer satisfaction.
• Facilitate secure agent capabilities for specific workflows, such as resolving transaction issues and recovering accounts, via internal application programming interfaces.
• Establish the technical architecture for vector databases, prompt engineering, and context handling to ensure highly personalized user assistance.
• Maintain operational excellence by monitoring system reliability, response latency, token usage costs, and fallback procedures for human agent escalation.

Requirements

• Possess at least eight years of software engineering background, with a minimum of two years in leadership roles managing high-performing teams.
• Have a proven track record of launching products driven by large language models, demonstrating deep knowledge of prompt engineering, model fine-tuning, and the broader AI provider ecosystem.
• Demonstrate experience in constructing RAG pipelines and managing the data lifecycle required to ground AI responses in real-time, accurate knowledge.
• Show strong expertise in systems and platform engineering, particularly in designing scalable distributed systems for high-traffic production environments.
• Be skilled in designing quantitative evaluation frameworks to assess hallucination rates, accuracy, and customer sentiment.
• Hold a deep understanding of AI safety protocols and guardrails, specifically regarding the prevention of personally identifiable information (PII) leakage and the avoidance of unsafe financial outputs.
• Exhibit proficiency in Golang and have hands-on experience with modern cloud-native infrastructure, including AWS and Kubernetes.

Nice to have

• Familiarity with vector databases like Pinecone, Weaviate, or Milvus, and orchestration frameworks such as LangChain or LlamaIndex.