Engineering Manager – Customer Experience AI

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Logo of Coinbase

Coinbase

1001 - 5000 employees

Founded 2012

₿ Crypto

💸 Finance

💳 Fintech

💰 $21.4M Post-IPO Equity on 2022-11

Crypto • Finance • Fintech

Coinbase is a leading cryptocurrency exchange platform that allows individuals and institutions to buy, sell, and trade various crypto assets such as Bitcoin and Ethereum. The company offers advanced trading tools, institutional solutions, and a self-hosted wallet for storing and managing cryptocurrencies. With a strong focus on security and transparency, Coinbase provides a trusted platform used by millions globally. It supports various features including staking, earning rewards, and spending crypto through their cards. Additionally, Coinbase provides developer tools and APIs for building onchain applications, making it a comprehensive hub for engaging in the crypto economy.

📋 Description

• Lead AI Strategy & Execution: Drive the roadmap for our conversational AI stack, moving beyond simple decision trees into LLM-driven reasoning, RAG, and agentic workflows. • Orchestrate the AI Ecosystem: Oversee the integration of third-party AI solutions while simultaneously scaling our in-house LLM infrastructure to handle high-stakes crypto support queries. • Build Evaluation & Guardrails: Establish rigorous AI evaluation frameworks (LLM-as-a-judge) and feedback loops to ensure our models are accurate, grounded, and compliant with global financial regulations. • Agentic Automation: Move from "chat" to "action" by building secure pathways for AI agents to perform complex tasks (e.g., transaction troubleshooting, account recovery) via internal APIs. • Drive Technical Architecture: Define how we handle vector databases, prompt engineering, and context window management to provide a personalised experience for every Coinbase user. • Operational Excellence: Own the reliability of AI services, including latency optimisation, cost management (token usage), and fallback mechanisms to human agents.

🎯 Requirements

• 8+ years of software engineering experience, with 2+ years leading high-performing teams in a fast-paced environment. • Hands-on AI/ML Leadership: Proven experience shipping products powered by Large Language Models (LLMs). You understand the nuances of prompt engineering, fine-tuning, and the current landscape of model providers (OpenAI, Anthropic, etc.). • Systems Thinking: Experience building RAG (Retrieval-Augmented Generation) pipelines and managing the data lifecycle required to ground AI in real-time knowledge. • Platform Mindset: You’ve built scalable, distributed systems and understand how to integrate AI components into a high-traffic production environment (Go, Ruby, or similar). • Evaluation Obsessed: You don’t just "vibe check" AI; you have experience with quantitative evaluation frameworks to measure hallucination rates, accuracy, and customer sentiment. • Security & Safety First: A deep understanding of how to build AI "guardrails"—ensuring models don't leak PII or hallucinate financial advice. • Nice to haves: • Experience with Vector Databases (e.g., Pinecone, Weaviate, Milvus) and AI Orchestration frameworks (e.g., LangChain, LlamaIndex) • Experience in FinTech or Crypto, specifically navigating the balance between AI innovation and strict regulatory/compliance requirements. • Background in NLP (Natural Language Processing) or traditional Machine Learning before the Generative AI boom. • Proficiency in Golang and experience with modern cloud-native infrastructure (AWS, Kubernetes).

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