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 and infrastructure that power Crosby’s AI-first legal platform. We work at the intersection of machine learning, product, and legal expertise to deliver intelligent, reliable systems that operate in high-stakes environments. We prioritize speed, ownership, and high standards — shipping quickly while maintaining rigor in everything we build.
The Role As a Data Scientist at Crosby, you'll play a critical role in developing the models and data systems that power our AI-driven legal platform. You’ll work across the full lifecycle — from data definition and labeling strategy to model development, evaluation, and iteration in production. You’ll partner closely with engineers, product teams, and legal experts to translate complex legal workflows into structured machine learning systems. Beyond modeling, you’ll help ensure our systems are accurate, reliable, and continuously improving in real-world use.
Responsibilities Develop evaluation systems: Build metrics, benchmarks, and experimentation frameworks to measure and improve model performance. Drive data strategy: Partner with legal and product teams to define labeling schemas, curate high-quality datasets, and improve data pipelines. Support production systems: Work closely with engineering to deploy models, monitor performance, and iterate based on real-world usage. Apply AI pragmatically: Leverage LLMs and other modern techniques to solve product problems, balancing sophistication with reliability and speed. Collaborate cross-functionally: Partner with engineering, product, and legal teams to deliver end-to-end systems that improve customer outcomes.