Machine Learning Engineer

Role Overview

At Coinbase, we are uncompromising on our mission to increase economic freedom. The CX Intelligence Engineering team builds the multi-agent platform powering Coinbase Chat, Help Center, and agent tooling. As a Machine Learning Engineer, you'll design and scale agentic systems that automate complex customer support workflows, connecting LLMs with internal APIs and tools.

Responsibilities

• 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.