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 platform. We operate at the intersection of backend systems, data, and AI — ensuring our platform is fast, reliable, and scalable. We prioritize strong foundations, ownership, and speed — building infrastructure that enables the entire team to move quickly without compromising reliability.
The Role As an Infrastructure Engineer at Crosby, you'll play a critical role in building and scaling the systems that power our platform. You’ll be responsible for designing and maintaining reliable, scalable infrastructure across compute, storage, and deployment systems. You’ll work closely with backend, AI, and product engineers to ensure our systems are performant, observable, and easy to operate. This role is for someone who cares deeply about reliability and developer velocity — building infrastructure that enables teams to ship quickly and confidently.
Responsibilities Build and scale infrastructure: Design and maintain cloud infrastructure across compute, storage, and networking. Own deployment systems: Develop and improve CI/CD pipelines and deployment workflows to enable fast, reliable releases. Ensure reliability and performance: Implement monitoring, alerting, and observability systems to maintain high uptime and performance. Support AI and data workloads: Optimize infrastructure for ML/AI systems, including batch processing, inference workloads, and data pipelines. Improve developer velocity: Build tooling and systems that make it easier for engineers to build, test, and deploy software. Collaborate across teams: Partner with engineering teams to ensure infrastructure supports product and system