Principal R&D Informatics and Scientific Systems Engineer

About the role

This role is positioned to grow with the organization. As our R&D footprint, data infrastructure, and use of AI expand, there is clear opportunity to take on broader scope, deeper technical ownership, and increased leadership responsibility.

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.

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.

Qualifications

• 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.

Skills

• Python for scripting, data manipulation, and automating or integrating scientific systems and workflows.
• AWS cloud services (e.g., S3, EC2, Lambda, and RDS).
• ELN/LIMS/registry platforms (e.g., Benchling).
• Data modeling and schema design.
• Scientific data structures, metadata, sample tracking, assay workflows, and data capture.
• AI tools and applications.
• Software engineering practices (version control, testing, release management).
• Genomics, sequencing, molecular biology, gene editing, or screening domains.
• BI tools and data visualization.

Benefits

Compensation: The stated base salary range represents Tessera’s good-faith estimate for this role. Actual compensation will be determined based on a number of factors, including but not limited to individual qualifications, years of relevant experience, internal compensation alignment, and external market data. Tessera offers a competitive package of base and incentive compensation as well as a comprehensive benefits program designed to support the health, wellness, and financial security of our employees and their families. Benefits currently include group medical, vision, and dental coverage; group life and disability insurance; a 401(k) plan with company contribution; tuition reimbursement; and more.

Pay

Per Year Salary Range: $167,000 - $200,000

Schedule

N/A