Principal R&D Informatics and Scientific Systems Engineer

Tessera Therapeutics•Somerville, MA

About The Position

This role is for a Principal R&D Informatics and Scientific Systems Engineer who is a scientifically minded systems expert, enjoying the intersection of research, technology, and innovation. The individual will have experience supporting scientific software platforms and understanding how R&D teams generate, manage, and use data throughout the research lifecycle. They will be comfortable partnering directly with scientists, computational biologists, and software engineers to translate scientific workflows into effective system solutions. As a hands-on member of the Research organization, this role will own and continuously improve the scientific systems ecosystem, including electronic lab notebooks, sample and data management platforms, workflow enablement tools, and emerging AI-powered solutions. The ideal candidate is a strong problem solver who enjoys configuring systems, improving processes, and learning new technologies that help accelerate scientific discovery.

Requirements

Scientific background in biology, chemistry, or a related life sciences discipline, with a strong understanding of biotech, pharmaceutical, or life sciences R&D workflows.
Experience configuring or supporting an ELN, LIMS, registry, or scientific workflow platform (e.g., Benchling or comparable systems).
Familiarity with scientific platform APIs, data warehouses, and integration patterns into downstream databases and analytics (e.g., 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.
Familiarity with AWS cloud services (e.g., 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.
Ability to work directly with scientists and translate experimental processes into system requirements.
Strong cross-functional collaboration across wet lab, dry lab, software engineering, and computational biology.
Strong documentation, communication, and prioritization skills.

Nice To Haves

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.

Responsibilities

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.