AI Research Infrastructure Engineer

Block is seeking an AI Research Infrastructure Engineer to architect and build intelligent systems for an AI-powered insights accelerator. This role involves designing AI agent ecosystems, automated data pipelines, and agentic workflows to reduce research cycle times. The engineer will build research infrastructure from scratch, combining AI/ML, research methodology, and data engineering to enable faster access to insights at Block.

Must Have

• Design, build, and deploy AI agents and agentic workflows.
• Automate research operations from study design through insights delivery.
• Design, build, and maintain automated data pipelines for research data.
• Architect ETL/ELT frameworks using Python, SQL or equivalent tools.
• Develop data models and schemas for research metadata.
• Design and prototype research automation systems using AI/ML techniques.
• Partner with engineering, design, and platform teams for system integration.
• 7+ years of experience in research, automation implementation, analytics, or related technical fields.
• 3+ years implementing AI/ML solutions, with experience in automation, LLM integration, or applied AI/analytics workflows.
• Hands-on technical skills in 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).
• Strong technical communication and translation skills.
• Proven ability to build stakeholder confidence and alignment during technology transformation.
• Strong project management skills.

Good to Have

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

Perks & Benefits

• Remote work
• Medical insurance
• Flexible time off
• Retirement savings plans
• Modern family planning

The Role

Block is scaling Customer Insights into an AI-powered insights accelerator that enables product and GTM teams to move at AI-first speed. We're seeking an AI Research Infrastructure Engineer to architect and build the intelligent systems powering this transformation by designing AI agent ecosystems, automated data pipelines, and agentic workflows that compress research cycle times while maintaining methodological rigor. You'll build research infrastructure from the ground up, working at the intersection of AI/ML, research methodology, and data engineering. You'll enable Block to access research & insights at the speed of decision-making. You are a technical builder who combines deep automation expertise with research domain knowledge, is comfortable working in a rapidly changing environment, and wants to define what an AI-powered research organization looks like at scale.

You Will

• 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.

You Have

• 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. Experience partnering with research teams to identify automation opportunities, educating stakeholders on AI capabilities and appropriate use cases, setting realistic expectations while fostering innovation, communicating technical challenges transparently with clear paths forward, and creating consensus across diverse teams with varying technical backgrounds and priorities.
• 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 preferred.