Responsibilities
- Design and develop systems that represent scientific workflows, including experiment design, protocol execution, sample tracking, and data capture across complex laboratory processes.
- Create secure, well-documented user interfaces and application programming interfaces that serve scientists, automated platforms, machine learning pipelines, and AI-based tools.
- Develop robust front-end and back-end services with an emphasis on speed, long-term maintainability, and system reliability.
- Construct comprehensive data models, database schemas, indexes, and data interface contracts across relational databases, NoSQL stores, vector databases, data lakehouses, and other scientific data platforms.
- Identify performance issues, resolve system bottlenecks, and enhance monitoring, fault tolerance, and operational stability of production environments.
- Utilize AWS cloud services, Kubernetes orchestration, and modern DevOps methodologies to deploy and manage scalable, secure systems.
- Collaborate with scientists, machine learning researchers, platform and data engineers, automation specialists, and product teams to convert research and operational requirements into functional software solutions.
- Participate in architectural planning, code reviews, test strategy, documentation standards, and engineering best practices to ensure high-quality software delivery.
