Principal R&D Informatics and Scientific Systems Engineer

Your experience includes… Scientific background in biology, chemistry, or a related life sciences discipline, with a strong understanding of biotech, pharmaceutical, or life sciences R&D workflows. , Benchling or comparable systems). , the Benchling REST API and Data Warehouse).

Data modeling skills, including designing and maintaining schemas and entity relationships and mapping them consistently across integrated scientific and engineering systems. Hands-on fluency with scientific data structures, metadata, sample tracking, assay workflows, and data capture. Experience using AI tools (or a demonstrated willingness and aptitude to learn them) and interest in applying AI to scientific and informatics workflows.

, S3, EC2, Lambda, and RDS) and how they support scientific data storage, integration, and compute workflows. Working proficiency in Python for scripting, data manipulation, and automating or integrating scientific systems and workflows. Strong analytical and problem-solving ability, exceptional attention to detail, and the judgment to work through ambiguous, cross-functional problems independently.

Requirements

Strong cross-functional collaboration across wet lab, dry lab, software engineering, and computational biology. Strong documentation, communication, and prioritization skills. It’s a bonus if you have… Hands-on experience with AI tools such as LLM-based assistants, AI coding tools, or AI-enabled scientific software.

Familiarity with software engineering practices: version control (git), testing, release management, change control. Experience with genomics, sequencing, molecular biology, gene editing, or screening domains. Familiarity with BI tools and data visualization.

You are interested in… Owning hands-on configuration, support, and continuous improvement of our scientific software environment, including our ELN/registry platform (currently Benchling): registry, entities, requests, assays, results schemas, and metadata structures. Owning scientific data models and schemas, and ensuring they map consistently across the ELN and integrated scientific and engineering systems. Partnering with Software Engineering to integrate scientific data into downstream databases and analytics.

Partnering with wet lab and dry lab teams to understand experimental processes, sample tracking, assay data capture, and sequencing workflows, and translating them into platform configuration. Supporting and enabling scientific AI tooling, helping scientists adopt AI capabilities within our scientific systems, providing training and guidance, and collecting feedback. Defining and enforcing scientific data capture standards, metadata consistency, workflow consistency, and change management for scientific systems.

Providing user support, training, documentation, troubleshooting, and adoption support. Troubleshooting and solving problems directly, working through ambiguous issues across systems, data, and scientific workflows with rigor and attention to detail. Identifying opportunities to simplify workflows, reduce manual effort, and improve data quality.

What success looks like... Scientists have reliable, well-supported systems that reflect how R&D work is performed. Scientific systems are maintained, documented, and improved through clear, repeatable processes.

Scientific data is captured consistently and is cleanly consumable by engineering and computational teams. AI tools are thoughtfully adopted and integrated into scientific workflows where they add real value. Scientific systems become less dependent on individual knowledge and more sustainable as team-owned platforms.