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Salary: USD 191,250 - 225,000 / annual
Coinbase is seeking a Senior Machine Learning Engineer to join the CX Intelligence Engineering team within the Customer Engagement and Experience organization. You will design and build AI-powered systems that power Coinbase's Help Center, chatbots, and agent workflows. The team owns a multi-agent platform serving Coinbase chat, Help Center, and agent tooling surfaces, working closely with Conversation Design, CX, and Engineering teams to deliver secure, compliant, and scalable AI support.
Key responsibilities include:
- Architect and deploy a multi-agent framework for agent assist, internal SOP execution, and secure LLM-powered actions, building systems that handle millions of customer interactions at scale.
- 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.
Coinbase is a remote-first (but not remote-only) company with quarterly in-person "surge" sessions for intense collaborative work.
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 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.
- Demonstrated ability to use generative AI tools responsibly (LibreChat, Gemini, Glean, etc.) 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.
