TetraScience is the Scientific Data and AI Cloud 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.
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.
As a Lead Platform Engineer, you will play a critical role in evolving and scaling our cloud-native Tetra data platform to handle 100× growth in data volume and user demand. You'll partner with engineering, data, and AI teams to design scalable architectures, proactively anticipate and mitigate scaling challenges, and ensure our platform remains performant, reliable, and cost-efficient as it grows.
This is a highly impactful role for an engineer who's passionate about distributed systems, understands the trade-offs of large-scale design, and thrives on turning ambitious scalability goals into concrete technical strategies.
If you're excited by the challenge of architecting cloud-native infrastructure to power massive growth and thrive on solving complex scalability problems, we'd love to hear from you.
Champion best practices in distributed systems design, scalability, and performance optimization, and share architectural insights through design reviews and technical documentation.