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Salary: USD 191,425 - 219,240 / annual
Superluminal Medicines is a generative biology and chemistry company using deep biology, computational chemistry, machine learning, and proprietary data infrastructure to accelerate small molecule drug discovery. The company is seeking a Staff Engineer to design and build the software systems that power experimental data workflows across the organization.
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 infrastructure connecting wet lab workflows to the broader data platform. This is a high-ownership role requiring you to design production systems, make architectural decisions, write high-quality software, and work directly with scientists to solve complex technical problems.
Key responsibilities include: designing, building, and operating production software systems supporting scientific data and wet lab workflows; owning the architecture and implementation of compound registration, experimental data ingestion, validation, transformation, and publishing systems; building reliable integrations across internal applications, APIs, cloud services, databases, and third-party scientific platforms; developing backend services, data pipelines, and internal applications used by scientists and research teams; designing systems that are observable, testable, resilient, and maintainable as workflows and data volumes scale; working directly with scientists to understand operational problems and translate ambiguous workflows into clear technical solutions; improving data quality and system reliability through validation frameworks, automated testing, monitoring, and well-defined interfaces; and establishing strong engineering practices around code quality, CI/CD, deployment, documentation, and operational ownership.
The ideal candidate is a strong software engineer excited by difficult integration challenges, evolving requirements, and building foundational systems in a modern drug discovery organization.
REQUIREMENTS:
Minimum qualifications:
- Bachelor's degree in Software Engineering, Computer Science, Information Technology, or related field; or equivalent professional experience
- 5+ years of professional software engineering experience building and operating production systems
- 3+ years of Python experience designing maintainable and well-tested software
- Experience building systems that integrate data across APIs, databases, files, asynchronous processes, and third-party platforms
- Experience with containers and modern deployment patterns using Docker and Kubernetes
- Experience designing scalable software architecture and making technical design decisions
- Experience designing and consuming REST APIs
- Experience developing production systems on Google Cloud Platform and cloud-native architecture
- Experience leading SQL queries, designing relational data models, and developing production data pipelines or workflows
- Experience developing and operating production software using automated testing, CI/CD pipelines, application monitoring, logging, and incident troubleshooting practices
Preferred qualifications:
- 8+ years of professional software engineering experience building and operating production systems
- Experience in biotechnology, pharmaceuticals, life sciences, or another technically complex scientific environment
- Experience with LIMS, ELNs, compound registration systems, laboratory informatics, inventory platforms, or scientific instruments
- Experience with drug discovery data, including compounds, assays, experimental results, and scientific metadata
- Experience with BigQuery, dbt, Dagster, or similar modern data platforms and orchestration tools
- Experience building internal tools or user-facing applications for technical users
- Experience with scientific Python, cheminformatics, or bioinformatics workflows
- Experience with infrastructure-as-code and cloud security best practices
- Experience modernizing manual, fragmented, or vendor-dependent operational workflows
