About us Gimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference. We combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it. We work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware. About the role As a Member of Technical Staff, you will build and optimize the low-level execution primitives that turn accelerator performance into production inference performance. Rather than optimizing for one hardware architecture, you will work across accelerators with different execution models, memory hierarchies, capabilities, and software stacks. Your work will shape the latency, throughput, and efficiency Gimlet can achieve across established and emerging hardware architectures. You will work close to the hardware across kernel implementation, memory access, execution behavior, profiling, and performance validation. You will develop optimizations that account for differences between accelerator architectures and partner with compiler, ML systems, and distributed systems engineers to improve performance across the full execution stack. What success looks like In the first 12-18 months, you will:
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Why join now? Gimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.
Agency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid.
