Gimlet Media

Member of Technical Staff - Kernels & GPU Performance

San Francisco, CA · Staff+

Sponsorship not specifiedDetected 134 days ago

Distributed Systems

About the role

At Gimlet, we believe every hire changes the company.
As a an early-stage 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.

Responsibilities

This role is an opportunity to help build the execution layer that transforms theoretical hardware performance into production reality.
Build and optimize kernels that improve latency, throughput, and hardware utilization for production AI workloads
Develop execution strategies that unlock performance across both established and emerging accelerator architectures
Partner with compiler, runtime, and distributed systems engineers to ensure end-to-end performance optimization

Nice to have

Improve memory efficiency, scheduling behavior, and execution characteristics across the inference stack
Influence how heterogeneous hardware is deployed and utilized within the next generation of AI infrastructure
Help establish performance engineering standards that shape the future of Gimlet's execution platform
Strong software engineering fundamentals
Experience working on performance-critical systems close to hardware
Comfort reasoning about low-level execution behavior, memory hierarchies, and performance tradeoffs
Experience with CUDA, Triton, CUTLASS, or other accelerator programming models
Deep understanding of GPU execution models (warps/wavefronts, blocks, grids)

Skills

Experience using profiling and performance analysis tools

Company info

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.
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.
large-scale AI datacenters and the orchestration platform that coordinates them.
As a an early-stage 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.
We are focused on making increasingly diverse compute work together.

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This listing is sourced directly from Gimlet Media's careers page and normalized into a canonical job model.