Sr Principal/Principal Software Engineer, App

Lila Sciences•Cambridge, MA

11h•$204,000 - $348,000

About The Position

Scientists shouldn't have to context-switch between a dozen tools to go from hypothesis to result. We're building the platform that makes this a reality — and we need engineers who want to solve problems no one has solved before. We're hiring Sr Principal / Principal Software Engineers to design the agents, interfaces, and platform integrations that let researchers seamlessly collaborate with AI. The Application Team sits at the center of LILA — the integration point where Machine Learning, Life Sciences, Physical Sciences, and Software become one AI-native experience that carries a scientist from hypothesis to experiment to breakthrough results. AI isn't a feature here — it's the architecture. Agent frameworks, tools, and LLM orchestration are core primitives, not bolt-ons. The problems are genuinely hard. Connecting AI to automated lab workflows, ML pipelines, and multi-domain knowledge graphs means inventing patterns, not copying them. You'll learn domains you never expected. Working shoulder-to-shoulder with lab scientists and ML engineers means your technical surface area grows fast. You'll ship things that matter. The tools you build accelerate research timelines from months to days. If you want to build at the intersection of AI and science, move fast without breaking trust, and grow into the kind of engineer who can architect systems that don't exist yet — we want to talk.

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

Applied AI Engineering: Experience building with AI agents, graph-based workflows, tool-use protocols (MCP), RAG pipelines, or LLM orchestration frameworks.
Cloud & DevOps Knowledge: 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 Python for Science: Familiarity with data science and ML libraries (pandas, numpy, scipy, jax, pytorch).
Domain Background: 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.