
51 - 200 employees
Founded 2015
🏥 Healthcare
💼 Consulting
📦 Logistics
Healthcare • Consulting • Logistics
TetraScience is a company dedicated to transforming raw scientific data into AI-native datasets for advanced scientific applications. By collaborating closely with leading biopharmaceutical companies, TetraScience enhances productivity, accelerates insights, and ensures data integrity across the scientific value chain. Their platform offers solutions for next-generation lab data management, AI-driven scientific outcomes, and compliance with industry standards. As the first company to provide a data and AI cloud built specifically for science, TetraScience enables its clients to liberate, unify, and transform their data, overcoming traditional data silos and boosting scientific productivity by providing a flexible, open, and collaborative infrastructure.
🕒 July 15
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51 - 200 employees
Founded 2015
🏥 Healthcare
💼 Consulting
📦 Logistics
Healthcare • Consulting • Logistics
TetraScience is a company dedicated to transforming raw scientific data into AI-native datasets for advanced scientific applications. By collaborating closely with leading biopharmaceutical companies, TetraScience enhances productivity, accelerates insights, and ensures data integrity across the scientific value chain. Their platform offers solutions for next-generation lab data management, AI-driven scientific outcomes, and compliance with industry standards. As the first company to provide a data and AI cloud built specifically for science, TetraScience enables its clients to liberate, unify, and transform their data, overcoming traditional data silos and boosting scientific productivity by providing a flexible, open, and collaborative infrastructure.
• Own the platform architecture, evolution and growth scaling across Enterprise Platform, Scientific Search, AI/ML Ops, Developer Platform, Developer Productivity, Lakehouse Platform, Partner Integrations and Cloud Infrastructure. • Set technical direction, own the decisions that cross team boundaries, and close architectural gaps before they become business risks. • Oversee enterprise Platform, Scientific Search, AI/ML Ops, Developer productivity, Lakehouse platform, and Cloud Infrastructure. • Ensure operational excellence based on a clear O11y architecture rolled out, with every production service having SLOs defined, monitored and managed. • Achieve strong product-market fit and traction while expanding industry partnerships and developer experience.
• 12+ years in software engineering, with at least 5 at staff or principal level in a SaaS platform or data infrastructure context. • 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. • 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. • 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. • 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. • Strong cross-team communication. You can write a document that produces alignment without a follow-up meeting to explain the document. • 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.
• Competitive compensation with equity • Unlimited PTO • Company-paid Life Insurance, LTD/STD • 401(k)
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