Own and evolve Braze’s ML platform for production-scale training, deployment, serving, observability, reliability, and cost efficiency. Lead complex infrastructure initiatives, including multi-region model serving, customer-specific model pipelines, CI/CD tooling, orchestration, and cloud identity. Set technical direction, manage incidents, collaborate across teams, improve engineering quality, mentor senior engineers and data scientists, and connect platform decisions to business outcomes.

Lead the technical vision and delivery of production machine learning systems, including distributed training, model serving, lifecycle management, and customer-specific model pipelines. Drive cross-team initiatives, establish engineering best practices, improve ML platform reliability and scalability, mentor senior engineers and data scientists, and connect technical decisions to customer and business outcomes.

Design and ship AI agents and automation for GTM workflows. Conduct stakeholder research, embed in field rhythms, own system architecture, reliability, adoption, and iterate to drive measurable revenue impact.