Sr Principal/ Principal Software Engineer, AI Lab Execution System

Your Impact at LILA

We are seeking a Senior Principal or Principal Software Engineer, AI Lab Execution System 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.
• Reliability, Performance, and Scale: Set technical standards for high availability, 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 Need To Succeed

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• 8-15 years of engineering experience building and deploying large-scale systems in production. You must be strong in either front-end or backend.
• Strong expertise in at least one of the following areas, with the ability to reason across all three: front-end engineering, backend engineering, or data modeling and system design.
• TypeScript, React, and Python: Strong experience building modern applications with React and TypeScript; Python experience is strongly preferred.
• Systems and Data Architecture: Deep experience designing scalable application architectures, APIs, domain models, schemas, indexes, data contracts, and distributed data systems.
• Databases: Strong experience with SQL and at least one of NoSQL, vector databases, graph databases, search systems, or data lakehouse architectures.
• API and Platform Design: Proven ability to design APIs, platform abstractions, and integration patterns that are reliable, maintainable, and easy for other teams to build on.
• Scientific or Data-Intensive Domains: Experience working in life sciences, materials science, ML platforms, laboratory systems, automation platforms, or other research-heavy and data-intensive environments.
• Operational Excellence: Experience designing production systems with strong observability, reliability, incident response, performance tuning, and long-term maintainability.
• Technical Leadership: Ability to mentor senior engineers, align stakeholders, make clear technical trade-offs, and drive complex initiatives from ambiguity to production.
• Communication and Collaboration: Strong listening skills and the ability to explain complex technical ideas to scientists, engineers, product leaders, and executives.
• Hands-on experience using AI coding assistants or AI-augmented engineering workflows to improve productivity.

Bonus Points For

• Orchestration Systems: Experience with orchestrators tools (Airflow, Prefect, Temporal, Dagster).
• Familiarity with Python for Science: Familiarity with data science and ML libraries (pandas, numpy, scipy, jax, pytorch).
• Experience designing systems that support auditability, traceability, reproducibility, data provenance, or regulated workflows.