Role overview

Scale AI is seeking an Infrastructure Engineering Manager to lead the team responsible for deploying and maintaining enterprise AI applications in production environments. As a leading AI data foundry for a decade, Scale works with major organizations across industry and government to build reliable AI systems that power real-world impact.

Key responsibilities

Develop and execute an infrastructure roadmap that aligns with business objectives and engineering priorities.
Lead the design and implementation of scalable, secure, and reliable infrastructure systems that support production deployments.
Establish and maintain service level agreements and objectives for platform uptime, performance, and developer experience.
Manage the engineering team, drive technical delivery, and mentor engineers to strengthen team capabilities.
Design, build, and optimize backend services for AI-driven applications, including AI agents, evaluation tooling, and automation systems.
Work cross-functionally with product, security, and engineering leadership to align priorities and translate customer deployment challenges into platform improvements.
Shape team culture, values, and processes as the team grows.

Requirements

At least five years of relevant experience, including a minimum of two years managing infrastructure or platform engineering teams.
Demonstrated experience with cloud platforms including AWS, GCP, Azure, or OCI.
Proven experience deploying and managing Kubernetes on on-premises infrastructure.
Deep knowledge of CI/CD pipelines, infrastructure-as-code tools, and container orchestration practices.
Track record managing production environments with high availability, reliability, and scalability demands.
Familiarity with monitoring, alerting, and incident response best practices.
Solid foundation and hands-on experience in network engineering.

What makes this role worth considering

You’ll join a team that owns the complete deployment lifecycle of AI applications in live customer environments, working closely with customers and cross-functional teams to ensure every deployment is production-ready and secure. This role bridges AI research and production, turning innovative prototypes into scalable enterprise solutions while staying connected to real-world customer impact and using that feedback to build lasting platform improvements.