Senior Machine Learning Engineer, CX

Coinbase

7h•$191,250 - $225,000•Remote

About The Position

As a Senior Machine Learning Engineer on the CX Intelligence Engineering team within Coinbase's Customer Engagement and Experience organization, you will design and build AI-powered systems connecting the Help Center, chatbots, and agent workflows. The team owns a multi-agent platform that powers Coinbase chat, Help Center, and agent tooling, working with Conversation Design, CX, and Engineering teams to deliver secure, compliant, and scalable AI support. The role drives CX automation initiatives including multi-agent framework development, agent-assist capabilities, and LLM-powered secure actions that help customers get answers faster and enable human agents to resolve cases more effectively. Coinbase is a remote-first, but not remote-only, company. Employees are expected to gather quarterly for in-person working sessions called “surges.”

Requirements

At least 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 with large language models, deep learning architectures, and the generative AI ecosystem, such as LangGraph, LangSmith, Google ADK, Vertex AI, or AWS Bedrock.
Demonstrated ability to build multi-agent or LLM orchestration systems.
Proficiency in Python and ability to write clean, maintainable, highly tested production code for ML applications.
Specialized depth in at least one of NLP, information retrieval, conversational AI, or advanced statistical modeling, and ability to ramp quickly on adjacent areas.
Ability to write technical design documents and clearly communicate complex ML concepts to non-technical stakeholders across product, design, and CX teams.
Responsible use of generative AI, with human oversight to deliver business-ready outputs and measurable improvements in workflow efficiency, cost, and quality.
Ability to responsibly use generative AI tools and copilots such as LibreChat, Gemini, and Glean in daily workflows, continuously learn as tools evolve, and apply human-in-the-loop practices.

Responsibilities

Architect and deploy the multi-agent framework for agent assist, internal SOP execution, and secure LLM-powered actions, building scalable systems that handle millions of customer interactions.
Own end-to-end execution of complex machine learning initiatives, managing priorities, technical trade-offs, and cross-functional dependencies from design through production delivery.
Build and optimize AI-driven concierge chat and agent copilot experiences to improve automated resolution rates and reduce case handling time.
Develop systems to detect and manage bad actors and adversarial inputs, maintaining confidence and safety across the multi-agent platform.
Establish best practices for system design, coding standards, and evaluation frameworks for ML/AI systems.
Mentor team members on architectural decisions and modern ML development workflows.