Sr Principal/ Principal Software Engineer, Scientific System of Record

Your Impact at LILA We are seeking a Senior Principal or Principal Software Engineer, Scientific System of Record to join our Scientific System of Record Team and help define and build the next-generation AI-driven scientific platform. In this role, you will serve as a technical leader for systems that connect scientific intent, laboratory execution, data capture, and AI-driven analysis. You will shape the architecture of user interfaces, services, high-performance APIs, databases, and reliability-critical systems that integrate advanced AI frameworks with complex scientific analytics and laboratory workflows. You’ll work closely with ML researchers, platform engineers, data engineers, product teams, and scientists to turn complex scientific processes into scalable, elegant software systems. These systems will need to support diverse workloads across structured SQL databases, data lakehouses, workflow engines, and lab execution environments. This is an opportunity to set technical direction for a cutting-edge AI platform with real scientific impact. If you are passionate about building high-leverage systems, mentoring strong engineers, and solving ambiguous problems at the intersection of AI, software, and science, we would love to hear from you. About The Team The Scientific System of Record Team (SSR) builds the memory layer for Lila's operations. It answers two questio [... source excerpt omitted ...] t scientific intent to physical reality. Together with the data and automation teams, their systems ensure reproducibility and close the Design-Build-Test-Learn (DBTL) loop. What You'll Be Building Technical Strategy and Architecture: Define architectural direction for the AI Lab Execution System and related Scientific System of Record capabilities, balancing long-term platform evolution with near-term product delivery. Lab Execution and Scientific Workflows: Design systems that model scientific intent, experiment planning, protocol execution, sample and asset state, operational events, and results capture across complex lab workflows. User Interfaces and APIs: Lead the des [... source excerpt omitted ...] low latency, observability, fault tolerance, and operational excellence Cloud and Infrastructure: Guide the use of AWS services, Kubernetes, and modern DevOps practices to build production-grade systems that scale across teams and workloads. Cross-Functional Influence: Partner deeply with scientists, ML researchers, platform engineers, data engineers, automation teams, and product leaders to translate scientific and operational needs into coherent platform architecture. Engineering Excellence: Mentor engineers, drive architecture reviews, raise the quality bar, and help establish patterns, tools, and practices that improve engineering velocity and system quality. What You'll N