Sr Principal​/Principal Software Engineer, Full Stack

Overview

Join us in shaping the future of science! We are seeking Sr Principal, and Principal Software Engineers with full stack experience to join our software org, where you’ll collaborate with software engineers, lab scientists, and machine learning engineers to build cutting‑edge tools for automated scientific analysis and more. Our teams span across App, Data, SSR, and LaS with a strong emphasis on Python development for scientific applications.

If you thrive in a collaborative, fast‑paced environment and bring best practices in git, development workflows, and user‑centered design, we want to hear from you!

They build the operating system for AI‑driven science. Their platform unifies Machine Learning, Life Sciences, Physical Sciences, and Software into one AI‑native experience — where a scientist can go from hypothesis to experiment to result in a single conversation. They ship the agents, the integrations, and the interfaces that make that possible.

They build 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, they close the Design‑Build‑Test‑Learn loop and ensure reproducibility.

They act as the physical and virtual execution layer for Lila's AI‑driven science. This connects the Lila App to AI Science Factories (AISFs), the mechanism through which experiments are dispatched to real instruments. This team owns the full stack of lab integration: orchestration of labflows, instrument integrations, bi‑directional data transfer, and the UI/UX that AI scientists and operators use to interact with the lab.

They build and support the data systems that underpin Lila's AI Science Factory™. Every experiment run in our labs, every measurement from an instrument, and every signal from our operational systems flows through the platform they build. Their work spans real‑time ingestion, large‑scale analytical storage, workflow orchestration, and the self‑service tools scientists, engineers, and ML teams use to go from raw measurements to discoveries.

They build the data backbone of Scientific Superintelligence™, so the science moves faster and each experiment makes the next one smarter.

• Design and build high‑performance, secure, and well‑documented UI and APIs that integrate with AI‑driven applications.
• Develop schemas and manage diverse data systems (SQL, No
  
  SQL, Vector DBs, and others) for optimal performance and scalability.
• Drive implementation of front‑end and back‑end services, focusing on performance, maintainability, and reliability.
• Diagnose and optimize system bottlenecks, ensuring high availability and low‑latency performance across large‑scale workloads.
• Leverage AWS services, Kubernetes and modern Dev Ops practices to build and deploy production‑grade systems at scale.
• Work with ML researchers, engineers, and scientists to integrate data pipelines, APIs, and cloud infrastructure into scientific workflows.

• 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 back‑end.
• Full Stack Development:
  Experience developing web apps across the full stack (React, Type Script, Monorepos like Nx, Tail Wind, FastAPI, SQL/No
  
  SQL, Python, Pydantic).
• Hands‑on experience using AI coding assistants to drive productivity is required.
• Communication &
  
  Collaboration:
  
  Acute 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 backend challenges, balancing trade‑offs between scalability, performance, and maintainability.

• Cloud & Dev Ops Knowledge:
  Hands‑on experience with AWS; strong understanding of Kubernetes and…