Job Description
We are hiring an IC4 Machine Learning Engineer to help evolve our conversational ecosystem by building a seamless hybrid vendor-internal chatbot experience. You will contribute to the design and implementation of a unified orchestration layer that coordinates interactions between vendor AI, internal multi-agent systems, and human participants. This role is ideal for someone who enjoys solving complex ML systems problems, building reliable handoff logic across LLM frameworks, and shipping AI-enabled products that are measurable and scalable.
What you’ll do:
Build and improve the orchestration layer that manages state transitions, context sharing, and intent routing across vendor and internal LLM frameworks in a distributed conversational environment.
Develop production-grade Python services that bridge advanced AI and ML capabilities with reliable customer-facing products.
Drive well-scoped ML projects from design through delivery, balancing technical trade-offs and collaborating across teams.
Contribute to system design, coding standards, and AI/ML development best practices across the team.
Partner with engineers and cross-functional stakeholders to build secure, scalable, and high-performing AI-enabled experiences.
Participate in design reviews and help ensure features meet Coinbase standards for security, compliance, and performance.
Required Skills and Experience:
2+ years of professional experience in machine learning and software engineering, with experience shipping production-grade ML services.
Hands-on experience building with modern AI architectures such as LLMs and deep learning, and familiarity with tools such as LangGraph, LangSmith, Google ADK, Vertex AI, or AWS Bedrock.
Strong proficiency in Python and the ability to write clean, maintainable, well-tested production code.
Working knowledge in at least one domain such as NLP, information retrieval, computer vision, or statistical modeling.
Ability to write technical design documents and communicate ML system designs clearly to cross-functional stakeholders.
Uses generative AI responsibly, applying human oversight to deliver business-ready outputs and measurable improvements in efficiency, cost, and quality.
