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Temporal is an open-source programming model that simplifies code, improves application reliability, and helps developers ship features faster. The company powers AI leaders including OpenAI, NVIDIA, Cursor, Lovable, and Replit, with expanding adoption across AI agents, data pipelines, and related applications.
The AI Foundations team is tasked with accelerating Temporal adoption across the ecosystem by combining deep use-case understanding with rigorous computer systems and software design principles.
In this Staff-level role, you will lead agent optimization efforts within Temporal. You'll design tools and mechanisms to help users build agents optimized for token spend and response time while maintaining result quality. The scope extends beyond model routing to include multi-agent architecture, cache policy, context management, and emerging agentic patterns. You'll work with a team focused on agentic development, ecosystem integrations, and policy/security systems.
Key responsibilities include:
- Design and build agentic coding systems with high reliability standards
- Develop deep expertise in AI application development patterns and emerging architectures
- Own end-to-end features from design through delivery, collaborating across teams
- Work across Python, TypeScript, Java, and Go
- Serve as domain expert on AI design patterns, advising field staff and the developer community
- Debug complex issues requiring expert attention and gather feedback on Temporal APIs
- Write technical documentation on Temporal concepts and APIs
- Engage directly with the developer community and occasionally support customers
- Travel once or twice yearly for team collaboration
Temporal is fully remote, open-source-first, and emphasizes written communication, thorough testing, and reliability. The role does not include on-call responsibilities, data science work, DevOps/SRE duties, or office-based work.
Required: 8+ years professional experience with proven expertise in optimizing AI systems using both machine learning and systems techniques.
