Principal Platform Architect

Join TetraScience, the leading scientific data and AI cloud for biopharma. As a Principal Platform Architect, you will own the platform architecture and drive its evolution and growth. This senior leadership role requires deep expertise in enterprise data and AI platforms, scientific search, AI/ML ops, developer platforms, and cloud infrastructure. You will set technical direction, make cross-team decisions, and close architectural gaps. Success in this role will be measured by the documentation and consistency of the authentication/authorization architecture, the clarity of the AI/ML infrastructure roadmap, the adoption of the developer platform, operational excellence, cost governance, and the evolution of the lakehouse platform architecture.

• Ownership of the platform architecture, evolution, and growth scaling across various domains.
• Setting technical direction, making decisions that cross team boundaries, and closing architectural gaps.
• Achieving strong product-market fit and traction, and leading the company into a growth scaling phase.
• Hands-on experience with data lake architectures at scale: Delta Lake or Apache Iceberg, schema evolution patterns, partition pruning, and the trade-offs between query performance and storage cost
• Infrastructure fluency on AWS with Kubernetes or ECS. You can read a cost anomaly report, trace it to a root cause, and produce an action within the same week
• Ability to write and defend architecture decisions: RFCs, trade-off documents, design reviews
• Demonstrated ownership of enterprise authentication and authorization systems at scale: SAML, OIDC, fine-grained RBAC across a multi-tenant SaaS product. You have been the person who got paged when auth broke, not just the person who designed it
• Comfort operating across strategy, architecture, and operations in the same week: setting a multi-year architecture direction and reviewing a runbook gap are both in scope
• Deep architecture ownership in at least one of the two fingerprint profiles above, with meaningful range across the other. Coverage of a majority of the eight domains is the bar
• 12+ years in software engineering, with at least 5 at staff or principal level in a SaaS platform or data infrastructure context
• Strong cross-team communication. You can write a document that produces alignment without a follow-up meeting to explain the document
• Hands-on experience with AI/ML serving infrastructure: you have built and operated model inference pipelines under production load
• Search architecture experience: you have designed and operated a search platform that handles diverse query types (keyword, semantic, or hybrid) across large structured or semi-structured datasets
• Experience in regulated industries (biopharma, medtech, financial services) where compliance and data residency are first-class architecture constraints built in from the start
• Familiarity with scientific data platforms, ELN/LIMS systems, or laboratory informatics ecosystems, including the structural constraints of instrument data
• Experience designing and operating internal developer platforms as a product: roadmap, adoption metrics, deprecation strategy
• Experience building partner integration programs at the architecture level: connector SDKs, reference implementations, integration certification criteria, and the developer experience that makes external parties self-sufficient
• Exposure to lab instrument ecosystems (proprietary data formats, on-prem agent deployment, vendor certification workflows) or analogous hardware-adjacent integration work in medtech or industrial IoT
• Prior experience as a founding or early platform architect at a Series B–D SaaS company scaling to enterprise.TBD