Machine Learning Engineer, CX Intelligence
CoinbaseReady to do the most impactful work of your career? At Coinbase, we are uncompromising on our mission to increase economic freedom. The bar is high, the environment is intense, and we like it that way. This isn't a place for complacency, it’s a place to be pushed past your perceived limits. If you're ready to build the future of finance alongside people who refuse to settle for "good enough," you belong here. Coinbase is a remote-first, but not remote-only company. Expect to get together quarterly for intense in-person working sessions called “surges.” learn more about working at Coinbase.
The CX Intelligence Engineering team, part of Coinbase's Enterprise Applications and Architecture org, builds the multi-agent platform powering Coinbase Chat, Help Center, and agent tooling. As a Machine Learning Engineer on this team, you'll design and scale the agentic systems that automate complex customer support workflows, connecting LLMs with internal APIs and tools to deliver fast, accurate, and compliant AI-powered experiences for millions of customers.
What you'll do:
- 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.
Required Skills and Experience:
- 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
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