Member of Technical Staff, Data Science

Crosby • New York, NY

2d • $200,000 - $300,000

About The Position

Crosby is an AI-native law firm that combines human expertise with technology to help companies sign commercial contracts faster. Crosby is building the first AI-first legal platform, reimagining corporate legal services. They are a team of technologists and legal experts who build proprietary technology and human-in-the-loop workflows that improve how lawyers and machines work together, delivering speed, consistency, and quality. Their 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. Crosby is backed by Sequoia, Index Ventures, and Bain Capital Ventures, and is building the end-to-end contracting platform for the next generation of fast-growing companies. The Engineering team builds the core systems and infrastructure that power Crosby’s AI-first legal platform, working at the intersection of machine learning, product, and legal expertise to deliver intelligent, reliable systems that operate in high-stakes environments. They prioritize speed, ownership, and high standards — shipping quickly while maintaining rigor in everything they build.

Requirements

• 1–5 years of experience in data science, machine learning, or applied NLP, ideally in a fast-paced startup environment
• Strong foundation in machine learning, statistics, and data analysis, with proficiency in Python
• Hands-on experience working with LLMs, NLP systems, or unstructured text data
• Experience working across the full ML lifecycle — from data curation and experimentation to deployment and monitoring
• Highly analytical with strong problem-solving skills and the ability to translate ambiguous problems into structured solutions
• Strong communicator who can collaborate effectively with both technical and non-technical stakeholders

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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed