Gimlet Labs is an applied research lab focused on building high performance AI systems. Backed by Menlo and Sapphire.
About the role
As a Member of Technical Staff, you will characterize and improve the performance of production AI inference across Gimlet's heterogeneous accelerator fleet. You will work across kernel execution, compiler output, runtime behavior, memory movement, synchronization, scheduling, and multi-device execution. Rather than treating performance as a collection of isolated microbenchmarks, you will connect hardware-level evidence to end-to-end latency, throughput, utilization, and cost. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Strong software engineering fundamentals and experience working on performance-critical systems
- Experience profiling GPU or accelerator workloads and turning measurements into production improvements
- A strong understanding of GPU execution, memory hierarchies, asynchronous execution, synchronization, and performance tradeoffs
- The ability to design controlled experiments, distinguish symptoms from root causes, and communicate performance findings precisely
- Bachelor's degree in a relevant field, or an equivalent combination of education, training, and professional experience
More about this role
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.
As a Member of Technical Staff, you will characterize and improve the performance of production AI inference across Gimlet's heterogeneous accelerator fleet.
You will work across kernel execution, compiler output, runtime behavior, memory movement, synchronization, scheduling, and multi-device execution. Rather than treating performance as a collection of isolated microbenchmarks, you will connect hardware-level evidence to end-to-end latency, throughput, utilization, and cost.
You will build the measurement systems and performance methodology that let Gimlet understand new hardware quickly, identify the limiting resource in real workloads, and turn that understanding into changes across kernels, compilers, runtimes, and distributed...
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