• Collaborate in the design, development and maintenance of robust backend applications and services to serve ML inferences (FastAPI/Flask or Node.js) • Build and optimize pipelines for real-time or batch inference processing • Deploy, monitor and optimize the performance of models in production, ensuring low latency and high availability • Contribute to the design of distributed systems capable of supporting intensive machine learning workloads • Deploy AI services using containerized infrastructure (Docker/Kubernetes) • Operate in cloud-based environments such as AWS • Work closely with Data Scientists and ML Engineers to translate research models into production-ready services • Support the identification and integration of emerging technologies to improve system performance and the end-user experience.

• Define and drive the technical roadmap for personalization and recommender systems, prioritizing roadmap items to meet business goals and defining short-term vision for the team. • Propose and deliver R&D that directly shapes roadmaps, multiple projects, and long-term deliverables. Models are used over the long term by multiple products and teams. • Design and lead the development of software used by multiple teams, ensuring long-term maintainability, scalability, and adaptability. • Ensure complex, multi-service personalization products meet SLAs and provide correct results over time. • Adapt systems to changing business needs and resolve multi-product, multi-team service incidents. • Establish and enforce experimentation best practices, including A/B testing frameworks, offline evaluation methodology, and metrics design across personalization surfaces. • Lead team meetings, ensure the team's progress on the roadmap, and make technical decisions that unblock projects. • Manage stakeholders' expectations with data-driven narratives and communicate effectively with senior leadership to align on strategy and track progress. • Drive organizational efficiency and business impact by implementing new technologies and processes. • Foster a collaborative and high-performance team culture. • Mentor senior and mid-level scientists, setting high code quality standards and best practices for the team. • Stay current with advances in recommender systems, LLMs for personalization, and representation learning, bringing relevant advances into production when they deliver measurable improvement.

• Architect multi-agent systems using advanced orchestration frameworks (LangGraph, Google ADK) to automate complex customer support procedures end-to-end. • Build and scale integrations using Model Context Protocol (MCP) to connect LLMs with internal Coinbase APIs, databases, and third-party tooling. • Develop automated "LLM-as-a-judge" evaluation pipelines to monitor, measure, and improve the performance of non-deterministic AI agents in production. • Implement RAG, fine-tuning, and prompt engineering techniques to ensure chatbot responses are grounded, accurate, and compliant with Coinbase policies. • Ship production-ready Python services that are resilient, low-latency, and capable of handling Coinbase-scale traffic across asynchronous microservices. • Partner with Conversation Design and Product to translate complex business logic into executable agent procedures within the decentralized architecture.

• Develop AI algorithms, prototypes and solutions for healthcare, with a focus on foundation models and self-supervised learning; • Optimise models and training pipelines for accuracy, scale and rapid experimentation; • Follow agile methodology and software engineering best practice, focussing on test-driven development, rapid prototyping, validation and iteration; • Provide regular technical and other progress reports relevant to projects, and ensure all progression is properly documented; • Engage with the literature to benchmark against and adopt state-of-the-art techniques and algorithms; • Rigorously evaluate generative AI models, and partner closely with teams training models at scale; • Contribute to a culture of excellence, helping to solve problems as they arise, instil a culture of best practice, integrity and agility, as well as champion the Harrison mission internally and externally.