Machine Learning Engineer, CX Intelligence
Coinbase · Remote - Brazil · posted Jul 7, 2026
mid
What this role actually asks for
Extracted by RemoteHuntMust have
- •3+ years building and scaling ML/AI systems
- •Experience with agentic systems (Loop, ReAct)
- •Proficiency in Python and microservices
- •Experience with LLM lifecycle and orchestration
- •Experience with vector databases
- •Ability to explain technical trade-offs
Tools and technologies
PythonLangGraphGoogle ADKPydanticJSONPineconeWeaviate
The full posting
About the Role
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
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