About TetraScience
TetraScience is the Scientific Data and AI Company building Tetra OS, the operating system for scientific intelligence. We help the world’s leading life sciences firms turn fragmented scientific data into AI-native assets and scientific workflows that accelerate discovery, development, and manufacturing. TetraScience’s growing ecosystem of strategic partners includes NVIDIA, Databricks, Thermo Fisher Scientific, Snowflake, Google, and Microsoft.
In connection with your candidacy, you will be asked to carefully review “The Tetra Way,” authored by our CEO, Patrick Grady; it is impossible to overstate the importance of this document, and you should take it literally as you decide whether our mission, culture, and expectations are right for you.
The Principal Informatics Architect 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 Do1. 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
Requirements
- Formal Informatics Training: Master’s + in Informatics (Clinical, Health, Bio, Chemi, etc.) or a related field (or equivalent formal academic training).
- Engineering Experience: 8+ 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.
- Experience managing & leading company wide migrations & reference architecture implementations
Benefits
- Competitive compensation
- Stock options in a VC-backed Series C company
- 100% employer-paid benefits for all eligible employees and immediate family members
- 401(k)
- Unlimited paid time off (PTO)
- Flexible working arrangements
- Company-paid Life Insurance, LTD/STD
Skills Required
- Master’s degree or higher in Informatics, Clinical Informatics, Health Informatics, Bioinformatics, Cheminformatics, or a related field, or equivalent formal academic training
- 8 or more years of professional software engineering experience
- Production software development experience, including SDLC, CI/CD, and software testing patterns
- Experience developing and exchanging data in Common Data Models, such as FHIR, OMOP, Allotrope, or CDISC
- Experience with Service-Oriented Architecture or microservices patterns
- Experience handling data consistency and contract evolution in distributed systems
- Experience with formal vocabularies and ontologies, such as OBO, CDISC Vocabulary Standards, SNOMED, LOINC, RxNorm, or BioPortal ontologies
- Experience normalizing disparate instance-level data and local terms to semantic standards
- Experience leading company-wide migrations and reference architecture implementations
- Experience with semantic formats and languages including OBO, OWL, and SPARQL
- Understanding of formal modeling, including cardinality, semantic properties, relationships, and constraints
What We Do
Biopharma R&D runs on data trapped in silos. Every instrument, ELN, and LIMS writes results in its own format, so most of what a scientist learns in one experiment never reaches the next. That's a big reason drug development costs keep climbing even as computing power gets cheaper. TetraScience builds Tetra OS, the operating system for scientific intelligence. It sits above and alongside a company's existing instruments, ELNs, and LIMS, works across any vendor, and turns raw scientific data into structured, AI-ready intelligence. Four components make it work. The Scientific Data Foundry ingests data from any source and converts it into governed, machine-readable data at scale. The Scientific Use Case Factory turns one-off workflows into reusable, validated ones deployed across sites. Tetra AI reasons and orchestrates on top of that foundation, grounded in real data instead of guessing. Sciborg teams, our embedded scientist-engineers, work inside customer organizations to get all of it adopted, not just installed. These parts compound. Better data makes workflows faster to build. Workflows generate better data. The result is intelligence that gets more valuable with use instead of resetting on every new project. Customers include Takeda, Bayer, Regeneron, Roche and and AstraZeneca, alongside partners like NVIDIA, Databricks, and Thermo Fisher Scientific. One top-20 pharma customer connected over 1,000 instruments, processed a petabyte of data in a year, and cut 20,000 hours of administrative work. Founded by Patrick Grady and Spin Wang, headquartered in Boston with a European base in Basel. Mission: radically improve and extend human life.
Why Work With Us
We're building the operating system underneath scientific R&D, not another point tool. Work touches real drug development at Takeda, Bayer, and Novartis. Every employee sees the same financials the board sees. We hire people who reason from first principles and run at hard problems.
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