About the role
About Crosby AI Crosby is an AI-first legal platform reimagining corporate legal services from the ground up. We are a team of technologists and legal experts who build proprietary technology and human-in-the-loop workflows that meaningfully improve how lawyers and machines work together — delivering speed, consistency, and quality across high-stakes work. Our systems review complex documents faster and with exceptional accuracy, combining advanced AI with structured legal expertise. Clients receive AI-powered redlines, commentary, and negotiation guidance within hours, at a predictable, volume-based price. Backed by Sequoia, Index Ventures, and Bain Capital Ventures, we're building the end-to-end contracting platform for the next generation of fast-growing companies.
The Team The Engineering team builds the core systems, infrastructure, and developer tooling that power Crosby’s AI-first platform. We work across backend, frontend, and AI systems to enable fast iteration, high-quality outputs, and reliable production systems. We prioritize speed, leverage, and strong engineering foundations — investing in systems that make the entire team more effective.
The Role As an AI Developer Experience Engineer at Crosby, you'll play a critical role in building the internal systems that enable our team to develop, evaluate, and ship AI features quickly and reliably. You’ll focus on improving the developer experience around LLMs, data, and experimentation — creating tools and infrastructure that accelerate iteration while maintaining quality. You’ll partner closely with engineers, data scientists, and researchers to streamline workflows across prompting, evaluation, fine-tuning, and deployment. This role is for someone who is obsessed with leverage — building systems that make other engineers dramatically more productive.
Responsibilities Build AI developer tooling: Design and implement internal tools for prompt management, evaluation, experimentation, and model iteration. Improve iteration speed: Create systems that enable rapid testing, debugging, and comparison of model outputs across different approaches. Own evaluation infrastructure: Develop frameworks for benchmarking, regression testing, and monitoring AI system performance. Streamline data workflows: Build pipelines and tooling to support data collection, labeling, and feedback loops for model improvement. Support production systems: Partner with engineering teams to ensure AI features are reliable, observable, and easy to maintain in production. Enable