Sr Principal/Principal Software Engineer, Full Stack

Lila Sciences•Cambridge, MA

About The Position

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! Our Software Teams The Application Team (App)- 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. The Scientific System of Record Team (SSR) - 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, their systems ensure reproducibility and close the Design-Build-Test-Learn (DBTL) loop. The Lab as Software Team (LaS)- 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. The Data Platform Team (Data)- They build and support the data systems that underpins 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. Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves. LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai. Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply. Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy. A Note to Agencies Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.

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.
Full Stack Development: Experience developing web apps across the full stack (React, TypeScript, Monorepos like Nx, TailWind, FastAPI, SQL/NoSQL, 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.

Nice To Haves

Hands-on experience with AWS; strong understanding of Kubernetes and containerization, infrastructure-as-code (Terraform, CloudFormation), and CI/CD pipelines (GitHub Actions).
Experience with ORMs: Experience with and web services for CRUD services (SQLModel, FastAPI, Django).
Orchestration Systems: Experience with orchestrators tools (Airflow, Prefect, Temporal, Dagster).
Familiarity with data science and ML libraries (pandas, numpy, scipy, jax, pytorch).
Exposure to laboratory software or analytics for life sciences, material sciences, or related fields.
Experience with laboratory devices, robotics, or hardware drivers.

Responsibilities

Design & Build UI and APIs: Design and build high-performance, secure, and well-documented UI and APIs that integrate with AI-driven applications.
Database Architecture & Scaling: Develop schemas and manage diverse data systems (SQL, NoSQL, Vector DBs, and others) for optimal performance and scalability.
Application Development: Drive the implementation of front-end and backend services, focusing on performance, maintainability, and reliability.
Performance & Reliability: Diagnose and optimize system bottlenecks, ensuring high availability and low-latency performance across large-scale workloads.
Cloud & 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.