Staff Software Engineer, AI Foundations (Agent Optimization)

ABOUT US Temporal is an open source programming model that can simplify code, make applications more reliable, and help developers focus on the important things like delivering features faster. We are on a mission to be the reliable foundation of every developer’s toolbox, and are building the team that will make that happen. Our values guide us—they are present in how we show up, make decisions, and work together to make an impact. We’re curious, driven, collaborative, genuine and humble. Temporal is growing and we are looking for those who share our values, challenge 'standard' thinking, and want to influence our future. If you have a passion for improving the developer experience, building world-class open-source software and communities, and want to be a part of our amazing team, we'd love to hear from you! Summary We have an opening to hire a Staff Software Engineer - Agent Optimization Temporal provides a reliable foundation powering AI leaders such as OpenAI, NVIDIA, Cursor, Lovable, Replit, and others. Its adoption is expanding to users spanning a broad range of AI applications ranging from agents to data pipelines and everything in between. The mission of the AI Foundations team is to accelerate Temporal adoption across the entire ecosystem. Our approach combines a deep understanding of use cases with rigorous application of computer systems and software design principles. In this role, you will lead our agent agent optimization efforts. You will design tools and mechanisms to help Temporal users build agents that are optimized for token spend and response time while maintaining result quality. Model routing is a first step, but represents the tip of the iceberg of techniques that we can apply. For example, multi-agent architecture, cache policy, and context management are all relevant. Candidates for this role should have direct experience with this problem domain. You will work closely with other AI Foundations team members, e.g., those who focus on agent