Your Impact at LILA

Join us in shaping the future of science! We are seeking Senior Software Engineers with backend experience to join our Data Platform Team (Data), where you’ll collaborate with software engineers, lab scientists, and machine learning engineers to build cutting-edge tools for automated scientific analysis and more. 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!

About The Team

The Data Platform Team (Data) builds 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.

What You'll Be Building

• Design & Build APIs: Design and build high-performance, secure, and well-documented 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.
• 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.

What You'll Need To Succeed

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• 5-8+ years of engineering experience building and deploying large-scale backend systems in production.
• 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 (SQL Alchemy, SQLModel, FastAPI, Django).
• Orchestration Systems: Experience with orchestrators tools (Airflow, Prefect, Temporal, Dagster).
• 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 deliver backend solutions, balancing trade-offs between scalability, performance, and maintainability.

Bonus Points For

• 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