Gimlet Labs is a startup that provides a multi-silicon inference cloud software to run and orchestrate AI workloads across diverse hardware types.
About Us Gimlet is building the next generation of AI infrastructure: large-scale AI datacenters and the orchestration platform that coordinates them. The future of AI will require vastly more compute than exists today. But as AI workloads become more complex and new hardware architectures emerge, simply deploying more GPUs isn't enough. The challenge is making increasingly diverse compute work together. Gimlet's platform intelligently partitions and routes workloads across heterogeneous hardware, enabling step-function improvements in performance and efficiency. Customers deploy through production-grade APIs without needing to think about hardware selection, placement, or optimization. We work with foundation labs, hyperscalers, and AI-native companies to power production workloads at massive scale and help define the infrastructure layer for the future of AI. ABOUT THE ROLE At Gimlet, we believe every hire changes the company. As a Series A company, talent density matters more than headcount. The engineers we hire today will shape the systems, culture, and standards that define Gimlet for years to come. The future of AI infrastructure will not be built on a single hardware platform. It will be built on systems capable of coordinating increasingly heterogeneous compute at unprecedented scale. This role is an opportunity to help build that future. We are not optimizing for headcount, we are optimizing for talent density. You will design and operate the distributed systems that schedule, route, and coordinate AI workloads across thousands of nodes and diverse hardware architectures. WHAT SUCCESS LOOKS LIKE In the first 12-18 months, you will help: - Build scheduling and orchestration systems that coordinate workloads across heterogeneous hardware - Improve reliability and fault tolerance for production AI infrastructure operating at scale - Create APIS and control planes that simplify deployment for customers running mission-critical workloads - Influence the architecture of a platform that will power the next generation of AI systems YOU MAY BE A GOOD FIT IF - Strong software engineering fundamentals - Experience building or operating distributed systems in production environments - Comfort reasoning about concurrency, failure modes, and tradeoffs in large-scale systems STRONG CANDIDATES MAY ALSO HAVE - Experience with Kubernetes or Kubernetes-adjacent systems beyond basic usage - Experience designing service-oriented architectures using RPC or asynchronous messaging - Familiarity with scheduling, queues, or resource management systems - Experience building reliable APIs and operating systems under high load - Software development experience in languages commonly used for systems development (e.g., Go, C++, Python) WHAT MAKES GIMLET DIFFERENT Most AI Infrastructure companies are focused on deploying more compute. We are focused on making increasingly diverse compute work together. We are not building another cloud platform. We are building the orchestration layer for the future of AI infrastructure. We believe the future of AI will require intelligent orchestration across many hardware architectures, datacenters, and execution environments. The systems we build today will help define how AI workloads are deployed for the next decade.