Who We Are

TetraScience is the Scientific Data and AI company. We are catalyzing the Scientific AI revolution by designing and industrializing AI-native scientific data sets, which we bring to life in a growing suite of next gen lab data management solutions, scientific use cases, and AI-enabled outcomes.

TetraScience is the category leader in this vital new market, generating more revenue than all other companies in the aggregate. In the last year alone, the world’s dominant players in compute, cloud, data, and AI infrastructure have converged on TetraScience as the de facto standard, entering into co-innovation and go-to-market partnerships: Latest News and Announcements | TetraScience Newsroom:

In connection with your candidacy, you will be asked to carefully review the Tetra Way letter, authored directly by Patrick Grady, our co-founder and CEO. This letter is designed to assist you in better understanding whether TetraScience is the right fit for you from a values and ethos perspective.

It is impossible to overstate the importance of this document and you are encouraged to take it literally and reflect on whether you are aligned with our unique approach to company and team building. If you join us, you will be expected to embody its contents each day.

The Senior Informatics Engineer will serve as a critical bridge between scientific meaning and software reality. You are a hybrid practitioner who possesses both formal informatics expertise and practical engineering capabilities.

In this role, you will be a key member developing & formalizing Semantic, Modeling, Knowledge Graph & Ontology Services while partnering with others to deliver successful integration across the Tetra Data Platform. This role is responsible for enabling the data in our platform to be both scientifically rigorous and architecturally scalable by incorporating formal semantic structure & relationships. You will build software and services that integrate with outside vocabulary sources as well as in flight data to define formal terminologies, vocabularies, and ontologies while enabling curation, modeling & integration with the rest of TetraScience’s ecosystem.

What You Will Do

1. Informatics Engineering & Service Architecture

• Apply Lessons Learned From Domain Leaders: Bring practical, everyday lessons learned and experiences from other areas, domains and professions to this problem space.
• Engineer Semantic Services: Co-design and build the Service-Oriented Architecture (SOA) components responsible for the lifecycle of semantic data. Your software enables ingestion, abstraction, curation, and publication, versioning, governance & distribution of semantic resources—encompassing formal biomedical ontologies, standard terminologies, and reference lists—integrating them directly into data-in-flight services.
• Data Engineering for Continuous Modeling: Develop and deploy ETL pipelines that lift instance-level data into formal vocabularies. You will ensure these pipelines capture the nuanced aspects of the model, including complex relationships, properties, and constraints.
• Living Ontology Development: Build services that project formal semantic meaning across the Tetra ecosystem, helping to formalize a "living," real-world ontology that evolves with our data and its usage.
• Partner as a Force Multiplier: Collaborate with Scientific Data Engineers, Architects, and Business Leads to integrate semantic artifacts technically. You will provide subject matter expertise, coaching, and training on the governance of controlled vocabularies, ensuring formal semantics and structure are applied consistently across platform applications.

3. Standardization & Governance

• Vocabulary Management: Develop software enabling management of the full lifecycle of our formal vocabularies. You will implement robust systems for versioning, deprecating, and migrating vocabularies across our customer base to ensure seamless operations.
• Engineering Mentorship: Serve as the "Informatics SME" for the engineering organization. You will partner with and mentor technical team members—ranging from pure software engineers to scientists—on how to leverage semantic artifacts to provide data with formal definitions, meaning, and context.
• Data Exchange Standards: Co-design the "handshake" protocols for data exchange between platform components. You will iteratively develop these standards to ensure that data leaving one system is deterministically understood by another, handling the complex mapping and transformation logic required for true syntactic and semantic interoperability.

Skills & Competencies

• Informatics & Engineering Hybrid: You can function with semantic artifact formats and languages (OBO, OWL, SPARQL, etc) as well as pure software constructs like API’s, Data Constructs, Object Oriented Programming, Source Control (git)(
• Formal Modeling Discipline: Informatician level understanding of modeling encompassing cardinality, semantic property and relationship modeling. You don't just "tag" data; you model the fundamental nature of the entities.
• SOA Fluency: Strong grasp of Service-Oriented Architecture. You understand how to distribute semantic logic across a distributed system to ensure scalability and maintainability.

Semantic Standards: Translational expertise in formal terminologies and semantic standards (FHIR Module 4, ISO 17117, SKOS, OWL, etc.) and when to leverage them

• Formal Informatics Training: Master’s + in Informatics (Clinical, Health, Bio, Chemi, etc.) or a related field (or equivalent formal academic training).
• Engineering Experience: 5+ years of professional experience in software engineering. You must have shipped production code and understand the software development lifecycle (SDLC), CI/CD, and testing patterns.
• Modeling Expertise: Proven experience developing and exchanging data in Common Data Models. You have likely built or significantly extended a standard model (e.g., FHIR, OMOP, Allotrope, CDISC Models, etc.) in a previous role.
• Architecture Skills: Demonstrated experience with Service-Oriented Architecture (SOA) or microservices patterns. You can explain how you’ve handled data consistency and contract evolution in a distributed system.
• Terminology Management: Experience working with formal vocabularies and ontologies (e.g., The OBO, CDISC Vocabulary Standards, SNOMED, LOINC, RxNorm, BioPortal Distributed Ontologies, etc.) and enabling the normalization of instance level disparate data (local terms) to semantic standards.