Machine Learning Engineer, CX Intelligence

🔥 7 minutes ago

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Coinbase

WebsiteLinkedInAll Job Openings

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

• Architect multi-agent systems using advanced orchestration frameworks (LangGraph, Google ADK) to automate complex customer support procedures end-to-end. • Build and scale integrations using Model Context Protocol (MCP) to connect LLMs with internal Coinbase APIs, databases, and third-party tooling. • Develop automated "LLM-as-a-judge" evaluation pipelines to monitor, measure, and improve the performance of non-deterministic AI agents in production. • Implement RAG, fine-tuning, and prompt engineering techniques to ensure chatbot responses are grounded, accurate, and compliant with Coinbase policies. • Ship production-ready Python services that are resilient, low-latency, and capable of handling Coinbase-scale traffic across asynchronous microservices. • Partner with Conversation Design and Product to translate complex business logic into executable agent procedures within the decentralized architecture.

🎯 Requirements

• 3+ years building and scaling ML/AI systems in production, with demonstrated experience implementing agentic "Loop" or "ReAct" based systems where AI takes actions autonomously. • Deep understanding of the LLM lifecycle including context window management, token optimization, structured output parsing (Pydantic, JSON mode), and multi-agent orchestration frameworks (LangGraph, Google ADK, or similar). • Strong proficiency in Python with hands-on experience in microservices architecture, asynchronous programming, high-throughput APIs, and vector databases (Pinecone, Weaviate, or equivalent). • Demonstrated ability to explain model behaviors and technical trade-offs to non-technical stakeholders across CX, Legal, and Product teams. • Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.