About this role

▸about Fireblocks — company background

The world of digital assets is accelerating in speed, magnitude, and complexity, opening the door to new ways for leveraging the blockchain. Fireblocks’ platform and network provide the simplest and most secure way for companies to work with digital assets and it trusted by some of the largest financial institutions, banks, globally-recognized brands, and Web3 companies in the world, including BNY Mellon, BNP Paribas, ANZ Bank, Revolut, and thousands more.

Digital assets and blockchain are rapidly transforming global finance, and Fireblocks provides the secure infrastructure and network that leading banks, fintechs, and Web3 companies use to move and manage digital assets at scale. Our mission is to enable any business to safely access and leverage digital assets and cryptocurrencies. About the team Join our global Data team, which partners closely with Product, Engineering, and Business stakeholders to power data-driven decision making and AI-driven products across Fireblocks. The team operates in a fast-paced, high-growth environment, building AI solutions, data foundations, and analytical frameworks that directly influence product strategy and customer outcomes.

What you'll do

Design and build LLM-powered systems that directly improve product capabilities and customer experience - enabling Product, Engineering, and Business teams to make faster, smarter, data-driven decisions at scale

Evaluate and quantify the business impact of AI initiatives through rigorous experimentation, benchmarking, and model evaluation to continuously optimize LLM performance

Continuously monitor, refine, and evolve AI models and solutions - iterating on prompt engineering and deployment practices to keep pace with rapidly advancing capabilities and shifting business needs.

Leverage AI-assisted development tools (e.g., Cursor, Claude Code) to accelerate delivery speed and engineering velocity across the team

Analyze large-scale datasets to surface strategic insights, define and track critical KPIs, and translate analytical findings into actionable product and business recommendations.