Lila Sciences is seeking a Senior Software Engineer to help build the next generation of their AI-driven scientific platform, focusing on designing and optimizing backend systems, data pipelines, and AI integrations to power intelligent, data-driven applications and solve humankind's greatest challenges in human health, climate, and sustainability.
Requirements
- 7+ years of professional experience building and scaling production systems, including APIs, data pipelines, and distributed services.
- Strong Python skills (FastAPI, Flask, Django), with solid experience in backend service development.
- Proven experience with SQL, NoSQL, and vector databases; skilled in schema design, indexing, and query optimization.
- Hands-on experience integrating ML models or AI-driven workflows into production services.
- Proficiency with AWS, Docker/Kubernetes, CI/CD pipelines, and infrastructure-as-code.
Responsibilities
- Design and deploy backend services and data pipelines that directly support advanced AI applications, including LLMs, RAG, and agentic frameworks.
- Build high-performance APIs and microservices that enable seamless integration between AI models, scientific tools, and user-facing applications.
- Architect and manage scalable pipelines capable of handling structured, unstructured, and vectorized data for AI/ML workloads.
- Implement and optimize SQL, NoSQL, and vector databases to support low-latency AI retrieval and inference workloads.
- Leverage AWS, Kubernetes, and infrastructure-as-code (Terraform/CloudFormation) to build robust, production-ready AI platforms.
- Diagnose system bottlenecks, optimize for cost and speed, and ensure the reliability and fault-tolerance of AI-driven workflows.
- Partner with ML researchers, platform engineers, and scientists to translate models and algorithms into scalable, production-ready systems.
Other
- Bachelorâs or Masterâs in Computer Science, Engineering, or a related field.
- Ability to work cross-functionally with diverse teams and explain complex technical concepts to non-experts.
- Experience working with life sciences, materials sciences, or other research-heavy fields.
- Comfort with fast-paced, iterative environments where impact and adaptability matter.
