Cresta is on a mission to turn every customer conversation into a competitive advantage by unlocking the true potential of the contact center. Our platform combines the best of AI and human intelligence to help contact centers discover customer insights and behavioral best practices, automate conversations and inefficient processes, and empower every team member to work smarter and faster. Born from the prestigious Stanford AI lab, Cresta’s co-founder and chairman is Sebastian Thrun, the genius behind Google X, Waymo, Udacity, and more. Our leadership also includes CEO, Ping Wu, the co-founder of Google Contact Center AI and Vertex AI platform, and CTO & co-founder, Tim Shi, an early member of Open AI.
We’ve assembled a world-class team of AI and ML experts, go-to-market leaders, and top-tier investors including Andreessen Horowitz, Greylock Partners, Sequoia, and former AT&T CEO John Donovan. Our valued customers include brands like Intuit, Cox Communications, Hilton, and Carmax and we’ve been recognized by Forbes and Bain Consulting as one of the top private AI companies in the world.
Join us on this thrilling journey to revolutionize the workforce with AI. The future of work is here, and it’s at Cresta.

ABOUT THE ROLE:

As a member of the infrastructure team you are responsible for designing, building, and advancing our core infrastructure that allows the engineering team to execute quickly, productively, and securely. You will join a collaborative but highly autonomous working environment in which each member has a defined role with clear expectations, as well as the freedom to pursue projects they find interesting.

RESPONSIBILITIES:

• Developer Toolchain. Partner with engineers to build dev tools that empower developer workflows and deployment infrastructure.
• Ensure reliability of multi-cloud Kubernetes clusters and pipelines.
• Metrics, logging, analytics, and alerting for performance and security across all endpoints and applications.
• Infrastructure-as-code deployment tooling and supporting services on multiple cloud providers.
• Automate operations and engineering. Focus on automation so we can spend energy where it matters.
• Building machine learning infrastructure that enables AI teams to train, test, and deploy on large-scale datasets.