About Superluminal Medicines:
Superluminal Medicines is a generative biology and chemistry company revolutionizing the speed and accuracy of how small molecule medicines are created. The Company’s platform aims to create candidate-ready compounds with unprecedented speed using a combination of deep biology, computational and medicinal chemistry, machine learning, and proprietary big data infrastructure. We are expanding the team of talented scientists who seek to build the future of small molecule drug discovery with creativity and innovation.
About the Role
We are seeking a Staff Engineer to design and build the software systems that power how experimental data moves through Superluminal Medicines. You will own critical systems at the intersection of software engineering, scientific data, and research operations, including compound registration, experimental data ingestion, scientific system integrations, and the infrastructure that connects wet lab workflows to our broader data platform.
This is a high-ownership engineering role. You will be expected to design production systems, make architectural decisions, write high-quality software, and work directly with scientists to solve complex technical problems. The ideal candidate is a strong software engineer who is excited by difficult integration challenges, evolving requirements, and the opportunity to build foundational systems in a modern drug discovery organization.
Key Responsibilities
- Design, build, and operate production software systems supporting scientific data and wet lab workflows.
- Own the architecture and implementation of systems for compound registration, experimental data ingestion, validation, transformation, and publishing.
- Build reliable integrations across internal applications, APIs, cloud services, databases, and third-party scientific platforms.
- Develop backend services, data pipelines, and internal applications used directly by scientists and research teams.
- Design systems that are observable, testable, resilient, and maintainable as scientific workflows and data volumes scale.
- Work directly with scientists to understand operational problems, identify root causes, and translate ambiguous workflows into clear technical solutions.
- Improve data quality and system reliability through validation frameworks, automated testing, monitoring, and well-defined interfaces.
- Establish strong engineering practices around code quality, CI/CD, deployment, documentation, and operational ownership.
