About the job
Gimlet Labs is pioneering the first heterogeneous neocloud specifically designed for AI workloads. As the demand for AI systems increases, the industry faces critical challenges related to power, capacity, and cost with the existing homogeneous infrastructure. Gimlet's innovative solution decouples AI workloads from hardware, effectively partitioning tasks into manageable components and directing them to the optimal hardware to maximize performance and efficiency. This strategy facilitates the creation of heterogeneous systems across various vendors and generations of hardware, including cutting-edge accelerators, leading to significant advancements in performance and cost efficiency at scale.
Building on this framework, Gimlet is developing a robust neocloud for agentic workloads, enabling clients to seamlessly deploy and manage their operations via stable, production-ready APIs—eliminating the need to navigate hardware selection, placement, or intricate performance optimizations.
We collaborate with foundational labs, hyperscalers, and AI-native enterprises to support production workloads that are designed to scale to gigawatt-class AI datacenters.
We are on the lookout for a Member of Technical Staff specialized in compilers. In this position, you will engage in the core compilation infrastructure that converts high-level AI workloads into highly efficient executable programs across a diverse array of advanced hardware. Your role will include designing and implementing compiler systems that partition workloads, optimize them through various Intermediate Representations (IRs), and target multiple execution environments and accelerators.
This position is ideal for engineers who thrive on building practical systems, engaging closely with hardware, and transforming emerging AI models and execution patterns into reliable, production-ready infrastructure.

