AI Research Infrastructure Engineer

• Design, build, and deploy AI agents and agentic workflows that automate research operations from study design through insights delivery, using LLMs, prompt engineering, MCP (Model Context Protocol) integrations, and workflow orchestration integrated with existing research and analytics tech stack
• Design, build, and maintain automated data pipelines that ingest, transform, and unify research data from diverse sources (surveys, transcripts, analytics, behavioral logs) into AI-ready repositories with RAG capabilities for instant insight access via tools like Goose
• Architect ETL/ELT frameworks using Python, SQL or equivalent tools to ensure data consistency, traceability, and scalability
• Develop data models and schemas for research metadata, participant data, and AI-generated insights to support efficient querying and analysis
• Design and prototype research automation systems using AI/ML techniques, partnering with design & engineering teams to productionize solutions
• Partner with engineering, design, and platform teams to integrate research automation systems with Block's tech stack (i.e. Goose, GitHub, etc.) and establish governance frameworks for quality, ethics, and compliance
• Mentor team members on AI agent development, agentic system design, and research automation best practices to build organizational capabilities in intelligent automation

• 7+ years of experience in research, automation implementation, analytics, or related technical fields with hands-on workflow optimization experience
• 3+ years implementing AI/ML solutions, with experience in automation, LLM integration, or applied AI/analytics workflows
• Hands-on technical skills in programming languages (Python, R, SQL) for automation development, API/MCP integrations, cloud platforms, and research data pipeline creation
• Experience with research and analytic platforms and tools (Qualtrics, Snowflake, etc) or transferable experience with analytics and automation platforms
• Strong technical communication and translation skills with ability to make complex AI/ML concepts, data architecture decisions, and automation workflows accessible and actionable for researchers, product managers, and business stakeholders
• Proven ability to build stakeholder confidence and alignment during technology transformation
• Strong project management skills with ability to coordinate multiple complex automation initiatives, manage competing priorities, and deliver measurable operational efficiency gains (reduced cycle times, improved quality outcomes, increased research capacity)

Familiarity with financial services, fintech, or payments industry research contexts and regulatory requirements

• Remote work
• medical insurance
• flexible time off
• retirement savings plans
• modern family planning