About the job
At Gimlet Labs, we are pioneering the first heterogeneous neocloud tailored for AI workloads. As AI technology evolves, the industry confronts critical limitations in power, capacity, and cost linked to the traditional homogeneous, vertically integrated infrastructure. Gimlet addresses these challenges by decoupling AI workloads from the fundamental hardware, intelligently partitioning them into components and orchestrating each to the hardware that best meets its performance and efficiency needs. This innovative approach facilitates heterogeneous systems across diverse vendors and generations of hardware, including the latest emerging accelerators, resulting in significant improvements in performance and cost efficiency at scale.
Building upon this platform, Gimlet is developing a production-grade neocloud for agentic workloads. Our customers can deploy and manage their workloads through stable, production-ready APIs without the complexities of hardware selection, placement, or low-level performance optimization.
Gimlet collaborates with foundational labs, hyperscalers, and AI-native companies to enable real production workloads designed to scale to gigawatt-class AI datacenters.
We are currently in search of a Technical Staff Member specializing in distributed systems. In this role, you will be instrumental in developing the core platform responsible for scheduling, routing, and managing AI workloads reliably at production scale. You will engage with systems that coordinate execution across thousands of nodes, provide stable production APIs, and guarantee predictable workload performance under real-world conditions of load and failure.
This position is ideal for engineers passionate about building foundational infrastructure, grasping end-to-end systems, and operating at scale.

