Senior Software Engineer, Scientific System of Record

About the role

Join us in shaping the future of science by taking ownership of the core systems that capture and connect scientific intent to experimental reality in this role. You will own the design and implementation of critical features within the Scientific System of Record, directly enabling reproducibility and the DBTL loop for scientific teams. This position requires a strong contributor who thrives in a fast-paced, collaborative environment and takes pride in writing clean, testable code. You will work hand-in-hand with lab scientists, machine learning engineers, and product teams to ensure the software meets the rigorous needs of automated science. If you are passionate about building user-centered systems that make scientists more effective, this is your opportunity. We value best practices in version control, development workflows, and operational excellence, and expect you to champion these standards across the team.

Key facts

What you'll do

Design and implement user interfaces and APIs that provide scientists and automation systems with reliable, secure, and well-documented interactions with lab data.

Build and maintain front-end and backend services using TypeScript, React, and Python, with a focus on performance, maintainability, and reliability in production environments.

Model complex scientific workflows, including experiment planning, protocol execution, sample and asset state tracking, operational events, and results capture across diverse lab processes.

Develop and evolve domain models, schemas, indexes, and data contracts across SQL, NoSQL, vector databases, and data lakehouse architectures to support scientific data.

Partner with cross-functional teams, including lab scientists, ML researchers, platform engineers, data engineers, automation teams, and product managers, to translate scientific and operational requirements into robust software solutions.

Diagnose performance bottlenecks and contribute to system observability, reliability, and operational excellence for production services that support critical scientific workflows.

Leverage AWS services, Kubernetes, and modern DevOps practices to build, deploy, and maintain production-grade systems that scale with scientific demand.

Contribute to architecture discussions, code reviews, testing practices, and documentation to uphold high engineering quality and shared standards.

Apply hands-on experience with AI coding assistants and AI-augmented engineering workflows to improve productivity and software quality.

Take ownership of ambiguous technical problems, make practical trade-offs, and deliver maintainable solutions that close the Design-Build-Test-Learn cycle.

Ensure data integrity and consistency across the memory layer that connects scientific plans to actual experimental execution and results.

Collaborate with the data and automation teams to ensure seamless reproducibility and traceability of scientific processes.

Write secure, well-documented APIs and UIs that support not only current scientific needs but also future AI-driven applications and analytical workflows.

Continuously improve system performance and scalability as the volume and complexity of scientific data grow within Lila operations.

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

4-6+ years of engineering experience building and deploying large-scale systems in production. You must be strong in either front-end or backend development.

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.

Strong experience building modern applications with TypeScript, React, and Python; Python experience is strongly preferred.

Experience designing, building, and maintaining APIs, services, and application components with a focus on reliability, performance, and maintainability.

Experience with SQL and at least one of NoSQL, vector databases, search systems, or data lakehouse architectures; familiarity with schema design, indexing, and query optimization.

Experience operating production software, including debugging, monitoring, performance tuning, and improving reliability over time.

Strong communication skills and a track record of working cross-functionally with engineers, product teams, scientists, or other domain experts.

Ability to take ownership of ambiguous technical problems, make practical trade-offs, and deliver maintainable solutions.

Hands-on experience using AI coding assistants or AI-augmented engineering workflows to improve productivity and software quality is required.