Staff Software Engineer, Scientific System of Record

Your Impact at LILA

We are seeking a Staff Software Engineer to join our Scientific System of Record Team and help build the next-generation AI-driven scientific platform.

You will focus on developing user interfaces, services, high-performance APIs, databases, and reliable systems that integrate advanced AI frameworks with complex scientific analytics and laboratory workflows. You’ll work closely with ML researchers, platform engineers, and scientists to develop systems that can handle diverse workloads and scale seamlessly, including structured SQL databases, data lakehouses, workflow engines, and lab execution environments.

This is an opportunity to apply your deep front-end and backend expertise to a cutting-edge AI platform with real scientific impact. If you are passionate about building performant, elegant systems, 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

• User Interfaces and APIs: Design and build high-performance, secure, and well-documented UIs and APIs that integrate with AI-driven applications.
• Database Architecture and Scaling: Develop schemas and manage diverse data systems, including SQL, NoSQL, vector databases, and other emerging technologies, for performance and scalability.
• Application Development: Drive implementation of front-end and backend services with a focus on performance, maintainability, and reliability.
• Performance and Reliability: Diagnose and resolve system bottlenecks while ensuring high availability and low-latency performance across large-scale workloads.
• Cloud and Infrastructure: Leverage AWS services, Kubernetes, and modern DevOps practices to build and deploy production-grade systems at scale.
• Cross-Functional Collaboration: Work with ML researchers, engineers, and scientists to integrate data pipelines, APIs, and cloud infrastructure into scientific workflows.

What You’ll Need to Succeed

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• 6–8+ years of engineering experience building and deploying large-scale systems in production.
• Strong expertise in at least one of the following areas, with the ability to work across the stack: front-end engineering, backend engineering, or data modeling and system design.
• TypeScript, React, and Python: Strong experience with React and TypeScript is required; Python experience is strongly preferred.
• Databases: Strong experience with SQL, NoSQL, and emerging database technologies such as vector databases; proven track record in schema design, indexing, and query optimization.
• API Development: Proven ability to design and scale RESTful or GraphQL APIs with a focus on reliability and performance.
• Hands-on experience using AI coding assistants to improve engineering productivity.
• Scientific or Data-Intensive Domains: Experience working in life sciences, materials science, or other research-heavy or data-intensive fields.
• Communication and Collaboration: Strong listening skills and a proven track record of working cross-functionally with scientists, data engineers, and product teams; able to explain complex ideas to diverse audiences.
• Problem Solving: Proven ability to take ownership of complex technical challenges while balancing trade-offs between scalability, performance, and maintainability.

Bonus Points For

• Cloud and DevOps: Hands-on experience with AWS, GCP, or Azure; strong understanding of Kubernetes, containerization, infrastructure as code such as Terraform or CloudFormation, and CI/CD pipelines such as GitHub Actions.
• Orchestration Systems: Experience with orchestration tools such as Flyte, Temporal, Airflow, Prefect, or similar systems.
• Experience building laboratory, scientific workflow, LIMS, ELN, data platform, or ML platform products.
• Experience designing systems that support auditability, traceability, reproducibility, data provenance, or regulated workflows.