Principal Platform Architect

The role TetraScience is building TetraOS - the category defining Scientific OS for biopharma. The Tetra Platform is the foundation for developing, delivering and operating our enterprise grade, secure, compliant Scientific Data and AI capabilities customers build upon. After achieving strong product-market fit and traction, TetraScience is entering a growth scaling phase where we are expanding our industry partnerships and developer experience to rapidly build the foundations of AI-native scientific data and workflows in production. In this role, you will own the Tetra Platform architecture, its evolution, and the partner integrations that extend it. This is an IC leadership role. You will define the platform architecture aligned with the product and business strategy. You will inform roadmap alignment decisions and trade-offs. The scope of this role is intentionally broad. We are looking for experienced candidates who have gone deep in two to three technology domains to gain breadth and depth of architectural, execution and operational experience. Your experience spans startups and large companies, so you understand the trade-offs at different stages and scales. Strong candidates will have deep, diverse experience with operational excellence and agility in building in a dynamic business context. The Tetra Platform spans the following diverse technology domains: Enterprise Data & Agentic AI Platform: Multi-tenant cloud architecture, Durable workflows, AWS, Databricks, RBAC/ABAC, IAM, observability and infrastructure chargebacks. Data, Knowledge, and Developer Products: Search, Semantic layer foundations, knowledge and ontology layers and products, external developer platforms. What you’ll own: Enterprise Platform: Tenancy, IAM, Compliance and control plane that enterprise customers use to govern their scientific data environment: SSO/SAML/OIDC, fine-grained RBAC / ABAC for SQL access. Scientific Search: Search architecture spanning keyword, semantic, and hybrid retrieval across scientific data, instruments, and metadata: relevance standards, indexing pipeline, and the infrastructure that makes search a reliable product surface. AI / ML Ops: Model lifecycle, inference and training on platform, Agentic IAM and DX, telemetry, observability and frameworks that keep scientific AI outputs traceable and operable under production load. Developer Platform: External builder platform, low and high code scientific solution development experiences, golden path tooling for partner integrations, SDKs and adoption metrics. Developer Productivity: Developer throughput as a first-class metric: toolchain ownership, local/prod environment parity, and friction reduction from commit to deployment. Acceleration and adoption of Tetra’s Scientific Use Case Library: Data Products and Workflows Platform, IDS design standards and evolution, and the data access layer that AI workloads and downstream pipelines depend on. Partner Integrations: Integration architecture for lab instruments and AI model partners: reference patterns, security boundaries, and the developer experience that enables self-service onboarding. Cloud Infrastructure: Production architecture, cost governance, and the observability layer from infra signal to customer-visible service health. What success looks like in year one Tetra’s Authn/Authz architecture is streamlined and well integrated across data, compute and Agentic AI platform layers. AI/ML infrastructure has a clear architecture and roadmap for MLE inference and training use cases, with strong operational telemetry, cost visibility and governance. The Developer Platform has streamlined experiences, SDKs and standard templates for scientific use cases to start from, with adoption and delivery by multiple scientific use case teams. A reusability architecture that enables compounding contributions from the ecosystem including Tetra, partner and customer builders. Operational excellence with a future-proof O11y architecture rolled