Lila Sciences is the worldâs first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method. We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before.
Requirements
- 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).
- 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).
Responsibilities
- Design and build high-performance, secure, and well-documented code that integrate with an ecosystem of existing services and apps.
- Diagnose and optimize system bottlenecks, ensuring high availability and low-latency performance across large-scale workloads.
- Leverage AWS services, Kubernetes and modern DevOps practices to build and deploy production-grade systems at scale.
- Work with ML researchers, engineers, and scientists to integrate data pipelines, APIs, and cloud infrastructure into scientific workflows and services.
Other
- 4-8 years of experience writing software in a commercial setting.
- 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.
- Domain Background: Exposure to laboratory software or analytics for life sciences, material sciences, or related fields.
