Sr Principal/ Principal Software Engineer, Scientific System of Record

About the role

You will define and deliver the core architecture for Lila's Scientific System of Record, owning the design and execution of systems that capture scientific intent, laboratory execution, and data-driven analysis. You will lead the technical strategy for user interfaces, services, high-performance APIs, databases, and reliability-critical infrastructure that tightly integrate advanced AI frameworks with complex scientific workflows. In this capacity, you will translate ambiguous scientific problems into scalable, elegant software solutions while establishing architectural guardrails across structured SQL databases, data lakehouses, workflow engines, and lab execution environments. You will mentor engineers, drive architecture reviews, and partner closely with ML researchers, platform teams, data engineers, product, and scientists to ensure reproducibility and close the Design-Build-Test-Learn loop. This role is positioned at the intersection of AI, software, and science, giving you direct influence over a cutting-edge platform that impacts real scientific discovery.

Key facts

What you'll do

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.

Model scientific intent, experiment planning, protocol execution, sample and asset state, operational events, and results capture across complex lab workflows into coherent software systems.

Lead the design of high-performance, secure, and well-documented user interfaces and APIs that serve scientists, automation systems, ML workflows, and AI-driven applications.

Establish durable domain models, schemas, and data contracts spanning SQL, NoSQL, vector databases, data lakehouses, and other scientific data systems.

Set technical standards for high availability, low latency, observability, fault tolerance, and operational excellence across reliability-critical systems.

Guide the use of AWS services, Kubernetes, and modern DevOps practices to build production-grade systems that scale across teams and diverse scientific workloads.

Partner deeply with scientists, ML researchers, platform engineers, data engineers, automation teams, and product leaders to translate scientific and operational requirements into coherent platform architecture.

Mentor engineers, drive architecture reviews, raise the quality bar, and establish patterns, tools, and practices that improve engineering velocity and system quality.

Champion end-to-end system design that connects laboratory execution, data capture, and AI-driven analysis into a unified and reproducible workflow.

Evaluate and integrate emerging AI frameworks and data platforms to ensure the architecture remains current with advances in scientific machine learning.

Collaborate with cross-functional stakeholders to prioritize features, manage technical debt, and align roadmap decisions with scientific impact and operational reliability.

Own key technical deliverables including system diagrams, design documents, API specifications, and operational runbooks that ensure clarity and continuity.

Implement robust data governance, security, and compliance practices tailored to scientific data, including sensitive sample and assay metadata.

Continuously monitor and improve system performance, scalability, and cost-efficiency across databases, workflows, and cloud infrastructure.

Requirements

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, in-process and distributed systems, and data models across structured and unstructured stores.

Data Platforms and Workflow Systems: Experience with data lakehouses, SQL and NoSQL databases, workflow engines, and lab execution environments that support complex scientific processes.

Cloud and Infrastructure: Hands-on experience with AWS services and Kubernetes, with a track record of building production-grade, scalable systems.

Reliability and Observability: Understanding of high availability, low latency, observability, fault tolerance, and operational best practices for critical scientific systems.