Senior Machine Learning Engineer, CX

As a Senior Machine Learning Engineer on the CX Intelligence Engineering team within Coinbase's Customer Engagement and Experience org, you'll design and build the AI-powered systems that connect our Help Center, chatbots, and agent workflows. This team owns the multi-agent platform powering Coinbase chat, Help Center, and agent tooling surfaces, partnering with Conversation Design, CX, and Engineering teams to deliver secure, compliant, and scalable AI support. You'll drive key CX automation initiatives, including multi-agent framework development, agent assist capabilities, and LLM-powered secure actions that help customers get answers faster while enabling human agents to resolve cases more effectively.

• →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 project execution for complex ML 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 that improve automated resolution rates and reduce case handling time.
• →Develop systems to detect and manage bad actors and adversarial inputs, ensuring the multi-agent platform maintains high confidence and safety at scale.
• →Establish best practices for system design, coding standards, and evaluation frameworks for ML/AI systems, mentoring team members on architectural decisions and modern ML development workflows.

• 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 (LangGraph, LangSmith, Google ADK, Vertex AI, or AWS Bedrock), with demonstrated ability to build multi-agent or LLM orchestration systems.
• Proficiency in Python with the ability to write clean, maintainable, and highly tested production code for ML applications.
• Specialized depth in at least one domain: NLP, information retrieval, conversational AI, or advanced statistical modeling, with the ability to ramp quickly on adjacent areas.
• Proven ability to write technical design documents and communicate complex ML concepts clearly to non-technical stakeholders across product, design, and CX teams.
• Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.
• Demonstrates the ability to responsibly use generative AI tools and copilots (e.g., LibreChat, Gemini, Glean) in daily workflows, continuously learn as tools evolve, and apply human‑in‑the‑loop practices to deliver business‑ready outputs and drive measurable improvements in efficiency, cost, and quality.

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