Lila Sciences is seeking a Principal Software Engineer to help build the next generation of their AI-driven scientific platform, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before.
Requirements
- 8+ years of experience successfully building and deploying scalable software systems in production environments.
- 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
- Lead End-to-End Software Development Lifecycle: Drive the technical design, implementation, and maintenance of software systems and applications.
- Architect & Implement Applications: Design and build robust, scalable web applications and services across the full stack, empowering scientists to harness AI in their research workflows.
- Collaborate Cross-Functionally: Partner with domain scientists, ML engineers, and product leads to integrate various technologiesâML models, data/compute infrastructure, and experimental automation tools.
- Establish Organizational Best Practices: Set standards for code quality, testing, and documentation. Mentor junior engineers and foster a culture of knowledge sharing.
- Operationalize Code in Production: Leverage observability tooling to monitor real world performance and steer improvements.
Other
- Bachelorâs or Masterâs degree in Computer Science, Engineering, or a related field.
- 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.
- Technical Leadership: Experience leading or mentoring a team and making key architecture decisions.
- Experience with laboratory devices, robotics, or hardware drivers.
- Domain Background: Exposure to laboratory software or analytics for life sciences, material sciences, or related fields.
