Sr Principal/principal Software Engineer, App

Lila Sciences Lila Sciences · AI Frontier · Alewife, Cambridge, MA +1 · Software

Sr Principal/Principal Software Engineer to design agents, interfaces, and platform integrations for researchers to collaborate with AI. The role involves building UI/APIs, database architecture, application development, performance optimization, and cloud infrastructure, with a focus on integrating AI into scientific workflows.

What you'd actually do

  1. Design & Build UI and APIs: Design and build high-performance, secure, and well-documented UI and APIs that integrate with AI-driven applications.
  2. Database Architecture & Scaling: Develop schemas and manage diverse data systems (SQL, NoSQL, Vector DBs, and others) for optimal performance and scalability.
  3. Application Development: Drive the implementation of front-end and backend services, focusing on performance, maintainability, and reliability.
  4. Performance & Reliability: Diagnose and optimize system bottlenecks, ensuring high availability and low-latency performance across large-scale workloads.
  5. Cross-Functional Collaboration: Work with ML researchers, engineers, and scientists to integrate data pipelines, APIs, and cloud infrastructure into scientific workflows.

Skills

Required

  • 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.
  • 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 have

  • 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.

What the JD emphasized

  • 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
  • Hands on experience using AI coding assistants to drive productivity is required

Other signals

  • AI-native experience
  • Agent frameworks, tools, and LLM orchestration are core primitives
  • Connecting AI to automated lab workflows, ML pipelines, and multi-domain knowledge graphs