Software Engineer
US$204,000 – US$348,000 / yr base salary
Posted 23 Mar 2026
Advertised as “Sr Principal/ Principal Software Engineer, Scientific System of Record”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 8–15 yrs
- Management level
- People management
- Contract
- Full-time
| Base salary | US$204,000 – US$348,000 / yr |
|---|
Field of studyComputer Science, Engineering (or related)
Study levelBachelor's or higher
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 questions:what did we plan to build? and what actually happened? These systems connect 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 design of high-performance, secure, and well-documented UIs and APIs that support scientists, automation systems, ML workflows, and AI-driven applications.
- Data and System Modeling: Establish durable domain models, schemas, and data contracts across SQL, NoSQL, vector databases, data lakehouses, and other scientific data systems.