Principal Full Stack Engineer

Lila Sciences•Cambridge, MA

5d•$204,000 - $272,000

About The Position

Join us in shaping the future of science! We are seeking an experienced full-stack senior software engineer to join our software platform team, where you’ll collaborate with software engineers, lab scientists, and machine learning engineers to build cutting-edge tools for automated scientific analysis and more. This role spans web services and data engineering, 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!

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
4-8 years of experience writing software in a commercial setting.
Full Stack Development: Experience developing web apps across the full stack (React, TypeScript, Monorepos like Nx, TailWind, FastAPI, SQL/NoSQL, Python, Pydantic)
Cloud & DevOps Knowledge: Hands-on experience with AWS, GCP, or Azure; strong understanding of Kubernetes and containerization, infrastructure-as-code (Terraform, CloudFormation), and CI/CD pipelines (GitHub Actions).
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 with Latest AI Tools: Exposure to AI technologies such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), or agentic frameworks, as well as experience leveraging AI to improve development performance.
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 Services and Front-ends: Design and build high-performance, secure, and well-documented code that integrate with an ecosystem of existing services and apps.
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 and services.