Teams at Laurel

Own AI features from problem definition through general availability, improving reliability and output quality for classification, narratives, work codes, retrieval, and vision extraction. Build evaluation systems, golden datasets, monitoring, semantic retrieval, embeddings, prompt optimization, and customer-specific generation. Collaborate with product, design, engineering, customers, and the AI Platform team to define success measures and deliver production ML/LLM features.

Designs, builds, and deploys AI-powered productivity systems for engineering teams. Responsibilities include developing CI/CD pipelines, integrating platforms and enterprise systems, evaluating emerging AI and infrastructure tools, improving developer workflows, collecting telemetry and feedback, driving adoption, and partnering with engineering leadership. The role requires strong platform, infrastructure, DevOps, cloud, Linux, and systems engineering expertise, plus hands-on experience with Kubernetes, AWS, Terraform, Go, TypeScript, and on-call operations.

Design, build, and maintain reliable backend systems; optimize performance, scalability, and security; implement testing and CI/CD; collaborate with product, design, engineering, and customer success; mentor engineers; contribute to technical direction and customer-focused requirements; and participate in on-call rotations.