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Senior Machine Learning Engineer, CX Intelligence

Company: Coinbase
Location: Remote – Brazil
Salary: $135K–$225K USD (base)
Employment Type: Full-time, Remote

About Coinbase

Coinbase is uncompromising on its mission to increase economic freedom. The company is a remote-first (but not remote-only) organization. Expect to get together quarterly for intense in-person working sessions called "surges."

About the Role

As a Machine Learning Engineer on the CX Intelligence team within Enterprise Applications and Architecture, you'll build the AI-powered conversational systems that connect Coinbase's Help Center, chatbots, and agent workflows. The team owns the multi-agent platform powering Coinbase Chat and agent tooling, partnering with Conversation Design, CX, and Engineering to deliver secure, scalable automated support. You'll lead the design and implementation of a unified orchestration layer that coordinates interactions between vendor AI, internal multi-agent systems, and human participants, directly improving how millions of customers get help.

Responsibilities

• Architect and deploy the orchestration layer that manages state transitions, context sharing, and intent routing across vendor and internal LLM frameworks in a distributed conversational environment.
• Build production-grade Python services that bridge advanced ML/AI research with reliable, measurable customer-facing products.
• Lead end-to-end project execution for complex ML initiatives, managing priorities, technical trade-offs, and cross-functional dependencies from design through delivery.
• Establish best practices for system design, coding standards, and AI/ML development workflows across the team.
• Mentor engineers on architectural integrity and modern AI/ML patterns, raising the technical bar for the broader team.
• Conduct design reviews to ensure every feature meets Coinbase's standards for security, scalability, and performance.

Requirements

• 5+ years of professional experience in machine learning and software engineering, with a track record of shipping production-grade ML services at scale.
• Hands-on expertise building with modern AI architectures (LLMs, deep learning) and the generative AI ecosystem, including frameworks such as LangGraph, LangSmith, Google ADK, Vertex AI, or AWS Bedrock.
• Deep proficiency in Python with demonstrated ability to write clean, maintainable, highly-tested production code.
• Specialized knowledge in at least one domain: NLP, information retrieval, computer vision, or advanced statistical modeling.
• Proven ability to write technical design documents and present ML system architectures to cross-functional stakeholders, translating complex technical concepts for non-technical audiences.
• Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.